business intelligence banking case study

How Business Intelligence in Banking Sector Works?

Pranay Agrawal   |   December 20, 2023 , 8 min read

Table Of Content

• Why is BI important for banks?

How does Business Intelligence Help Banks?

Applications of bi in banking, frequently asked questions, further reading: business intelligence applications across sectors.

Business Intelligence (BI) has revolutionized the banking sector, profoundly impacting data management and decision-making processes. Within the realm of data analytics, BI tools empower banks to efficiently collect, analyze, and utilize data for strategic insights.

BI’s significance in banking is immense, enabling financial institutions to process millions of transactions in seconds. To put this into perspective, these tools can handle data equivalent to around 100,000 hours of continuous banking operations per minute, showcasing their transformative power in decision-making within the financial world.

BI tools have reshaped banking from simply storing customer information to effectively managing data via digital resources, mobile apps, and websites. With a staggering estimated daily data volume of 2.5 quintillion bytes , BI solutions offer a dynamic approach, optimizing operations and driving competitiveness in the banking industry.

Business Intelligence plays a pivotal role in the banking sector, reshaping the way financial institutions operate, strategize, and engage with their customers. In today’s data-driven landscape, BI tools have become integral to banks for reasons including risk reduction, gaining a competitive edge, optimizing marketing strategies, and enhancing operational efficiency.

Here’s a breakdown of key reasons why BI is indispensable for banks:

• Risk Reduction: BI tools track customer behavior, aiding in fraud detection and ensuring regulatory compliance. They also analyze financial data to manage credit portfolios effectively and identify potential delinquencies, addressing the critical need for BI in Banking.
• Competitive Edge: BI enables personalized customer experiences, identifies new investment opportunities, and tailors products to meet individual customer needs, highlighting its essence in banks’ solutions.
• Optimized Marketing: These tools analyze metrics like email performance and advertising spending, optimizing marketing strategies for banks.
• Operational Efficiency: BI solutions automate manual processes, minimizing errors, and streamlining workflows, ultimately reducing costs and enhancing productivity—an essential aspect of BI in banking.
• Enhanced Customer Experience: BI services contribute to improved customer satisfaction and retention by creating targeted campaigns and maximizing upselling opportunities through profitability and sentiment analysis, underscoring its importance in finance.

Also Read: Role of Business Intelligence in Finance

BI significantly benefits banks by leveraging data to enhance operational efficiency, mitigate risks, and facilitate informed decision-making. Key aspects include:

Real-Time Reporting : Business Intelligence enables the visualization of historical and real-time data, uncovering consumer behavior patterns and operational bottlenecks. For instance, they facilitate:

• Live data acquisition for managing various bank branches efficiently.
• Monitoring average wait times in queues to enhance customer service.
• Real-time feedback on the utilization of counters and tellers.
• Snapshot provision for booked or recent appointments, optimizing scheduling.

Data Mining : This crucial BI component cleanses, standardizes, and identifies patterns within vast datasets, used in banking for:

• Reducing Financial Loss with Default Detection: Identifying potential loan repayment defaults and enhancing credit scores.
• Targeted Marketing: Categorizing potential clients based on various attributes and preferences for cross-selling opportunities.
• Improved Anti-Money Laundering Management: Identifying transactional trends and minimizing false patterns.

Cash Flow Management : BI aids effective cash flow management, assisting banks in:

• Identifying areas for cost optimization and ensuring near real-time financial transaction monitoring.
• Streamlining operations, optimizing resource allocation, and maintaining service quality, even during budget shortfalls.

Predictive Analytics : Leveraging historical and current data to forecast future outcomes and identify patterns, banks utilize predictive analytics for:

• Credit Scoring: Assessing creditworthiness for improved lending decisions.
• Customer Retention and Loyalty: Predicting and enhancing customer loyalty.
• Regulatory Compliance: Ensuring adherence to financial regulations.
• Risk Modeling and Fraud Detection: Identifying potential risks and mitigating fraudulent activities.

The integration of predictive analytics enables banks to employ data-driven models, ensuring operational profitability and competitiveness in the market.

Also Read: 11 Key Benefits of Business Intelligence

Business Intelligence (BI) has significantly reshaped the banking sector, revolutionizing operations and decision-making processes. Within this industry, BI serves as a pivotal tool, aiding banks in various crucial aspects, such as fraud detection, customer segmentation, credit risk assessment, and regulatory compliance.

1) Fraud Detection and Prevention:

• Real-time Transaction Analysis: Banks leverage BI to scrutinize vast transactional data instantly, pinpointing irregularities or suspicious activities.
• Pattern Recognition: BI tools identify unusual patterns and anomalies, aiding in detecting potential fraudulent behavior swiftly.
• Risk Mitigation and Customer Trust: By swiftly addressing fraudulent activities, banks minimize financial losses, bolstering customer trust and confidence in their services.

2) Customer Segmentation and Profiling:

• Data-Driven Insights: BI extracts valuable insights from customer data, enabling banks to understand preferences, behaviors, and needs.
• Tailored Services: Utilizing BI, banks customize their offerings, aligning products/services with specific customer requirements, boosting satisfaction.
• Enhanced Marketing Opportunities: BI-driven segmentation identifies opportunities for cross-selling, maximizing the value of customer relationships.

3) Credit Risk Assessment and Management:

• Informed Decision Making: BI analyzes credit-related data points to make informed lending decisions, reducing the risk of defaults.
• Proactive Risk Management: Real-time monitoring helps banks identify potential default risks among existing customers, allowing for timely risk mitigation.
• Improved Portfolio Performance: BI aids in managing credit risk effectively, contributing to better overall portfolio performance and stability.

4) Regulatory Compliance:

• Automated Compliance Processes: BI automates data collection, analysis, and reporting, ensuring banks adhere to regulatory frameworks like AML and KYC.
• Risk Reduction: By streamlining compliance processes, BI minimizes the risk of non-compliance penalties, saving time and resources.
• Efficiency Gains: BI-driven automation enables banks to focus resources on core operations while ensuring adherence to regulatory standards effectively.

In conclusion, the multifaceted applications of Business Intelligence have fundamentally transformed the banking industry. From combating fraud and enhancing customer relationships to minimizing risks and ensuring compliance, BI’s integration with data analytics has empowered banks to operate more efficiently, strengthen customer trust, and maintain a competitive edge in today’s dynamic banking landscape.

Empowering Banking Insights: Key BI Tools for Financial Optimization

1. Zoho Analytics:

Zoho Analytics is a self-service BI tool used by over 2 million users in banking. It provides customizable reports, and in-depth financial insights, and tracks financial KPIs. Explore Zoho Analytics

DOMO is a cloud-based BI tool excelling in data integration, offering automated data refresh, rich visualizations, and an alert system. Discover DOMO

3. Trademo Intel:

Trademo Intel aggregates global import-export data, aiding in supply chain analysis, prospecting global companies, and risk modeling for banks. Learn about Trademo Intel

These tools offer diverse functionalities, assisting banks in swift data analysis, operational optimization, and risk management across various banking sectors.

In conclusion, Business Intelligence (BI) has reshaped banking, redefining decision-making with its transformative capabilities. From handling vast transaction volumes to enabling personalized customer experiences, BI tools like Zoho Analytics, DOMO, and Trademo Intel empower banks across risk management, compliance, and customer-centric strategies. BI’s pivotal role in driving operational efficiency and strategic insights remains paramount, ensuring banks adapt to dynamic market shifts.

As banking continues evolving, BI’s integration propels institutions toward agility and competitiveness. The symbiotic relationship between data intelligence and banking not only enhances operations but also fosters a culture of informed decision-making.

Embracing BI’s potential unlocks actionable insights, shaping a future where data empowers sustainable growth and resilience within the banking sector.

Explore scalable BI solutions for your BFSI needs with Scaleupally’s expertise!

Q: How can business intelligence transform the banking industry?

Business intelligence empowers banks to derive valuable insights from customer data, facilitating real-time decisions with accuracy. It propels profitability in banking through streamlined data handling, improved workflows, and enhanced customer retention.

Q: Is Business Intelligence better suited for Finance or IT departments?

Business Intelligence, as a methodology, doesn’t belong exclusively to Finance or IT. Rather, it’s a versatile tool used by anyone leveraging business data to make customer-centric decisions. While it’s broad-based, specialized groups supporting this methodology usually focus on strategic work.

Q: What benefits does business intelligence offer to the financial industry?

Business intelligence tools aid the financial industry by simplifying data collection, visualization, monitoring, and analysis. They enable the measurement of financial performance, uncover improvement opportunities, and facilitate sustained growth by deciphering complex financial data into meaningful insights.

1.  Business Intelligence in Ecommerce

2.  Business Intelligence in Supply Chain

3.  Business Intelligence in Finance

4.  Business Intelligence in Travel Industry

5.  Business Intelligence in Retail

6.  Business Intelligence in Manufacturing

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• Banking Business Intelligence Case Study

BI Case Study: A Global Capital Markets Bank

A multi-national global bank headquartered in Australia engaged InetSoft for a bank-wide business intelligence initiative to address challenges experienced in their global expansion.

Organization Background

The bank is a global organization with businesses spanning investment banking, institutional trading & sales, corporate financing, asset management and retail banking. The business operates in all continents with major commerce centers in Asia, Europe and North America. Business is conducted around the clock globally. Books and operations are passed among major commerce centers with overlapping time zones. Relationships among business groups are multifaceted and dynamic.

Even with such a linked business model, each locale of operation requires flexibility so that their local market demands can be met in a timely fashion without causing global impacts. Due to the span of their operations, business groups are required to comply with different laws and regulations. For regulatory, legal, business or technical reasons, certain aspects of the business must be segregated and secured.

IT Operations Background

IT operations supporting business groups are dispersed in multiple continents' technical centers. They are responsible for ensuring the architecture and data integrity of all business systems. Security, compliance and compatibility are also important priorities. Many corporate-wide data and computer resources are owned and operated by IT. Business groups must share data and resources for bank wide consistency. With a mixed development model, both in-house IT units and vendor engineering teams are working closely.

After development and implementation, the goal is to hand over finished systems smoothly to in-house IT operations. For business group-owned information technology projects, business groups typically take ownership of the business requirements and processes. IT groups will ensure the project's integrity and compatibility. If no in-house development resource is provided, vendors are expected to work with analysts from business groups with only back-end support provided by IT

Owner Business Group Background

The project owner is a fast growing trading facilitation group in the bank. The main business focus of the group is to provide trading services and financing for institutional clients. The financial products traded are derivatives which consist of complex parameters. Hundreds of clients are scattered throughout the world and trade around the clock.

The group itself has corresponding local operations in major commerce centers to facilitate the business. In this fast paced business, the trading environment can quickly change direction. Both bank and client employees need an accurate understanding of their positions. Furthermore, historical analysis of past trading activities, with the ability to drilldown into the individual trade level, is important.

Business Challenges

The bank employees and clients have been generating reports from the transactional system that records all trades. The system has multiple pain points. First, the conflict of serving transaction needs and business intelligence needs is causing system overload that many times delays trading execution. This can be costly to clients and the bank. Second, the system is designed for transactions. Therefore reporting is very slow. This is not only a pain but causes lost business opportunities. Third, inflexible reports can't be easily customized by users who many times resort to manually produced reports. Manual reporting is not matching trading demands but also is error prone. The fast growing client base really pushed current process to the limits.

Considered Architecture

The first considered solution was to build a data warehouse with associated ETL. After consulting with groups using data warehouses in the bank, it was clear that this solution would have serious shortcomings. Data warehouses are static modeled. This means it has a pre-defined data structured according to current known business needs. Constant reporting requests under current system has clearly demonstrated that needs can't be so well defined statically. This clearly doesn't fit derivative trading which is a quick moving target.

The second issue is the delay introduced by ETL and a data warehouse. Certain reports must be real time to reflect up-to-the-second positions. The challenge is to find a front end tool that can mix reporting against a transactional database and a data warehouse. Also, a data warehouse is normally self-contained which makes data linkage with bank wide data and systems even more challenging.

InetSoft's Architecture

After evaluating these challenges, InetSoft proposed an architecture that dynamically creates a data hub in the place of data warehouse. This data hub is incrementally populated. Instead of a predefined static schema, its creation is driven by visual reports and dashboards. Basically, it is a dynamic data warehouse. The ETL process is baked into the visual design process which produces visual reports as well as reusable data blocks.

Visual reports can be iteratively designed against the transactional database's schema. When it is deployed into production, the system materializes the underlying data queries into analytic-oriented, high performance data blocks residing inside the data hub. These data blocks remove the reporting load of querying the transactional database. The data blocks also deliver high performance because they are optimized for these reports. For real time reports, InetSoft's software can simply materialize, or cache the results of, the underlying queries and retain the live connection to the transactional database.

Other Key Features

Beside architecture considerations, the bank valued the following unique built-in features:

Multi-tenancy

Each client is set up as a tenant and requires total isolation. On the other hand, bank account managers need to able to see across his/her own accounts. Higher level management needs access across different group or all accounts.

Paginated Reports

Visual reports and dashboards are great for interactive use. But they do not replace the need for paginated reports which are suited for client statements and executive summaries.

Cascading Parameters & Wizards

For casual users, these functions are proven to be the easiest entrance points for self-service.

Pure-browser Visualization Design

Power users are from all different client organizations. It is impossible to install and manage desktop design tools in this situation. Only a 100% browser based tool makes power user self-service possible.

Data Hub/Data Blocks

he fact that the visual report design process creates data blocks that are not tied to the single report is very useful. The data hub only contains data blocks proven useful by its first associated report. Data blocks can all be reused and mashed up for other reports which makes it possible for less experienced users to do sophisticated self-service reporting.

Fault Tolerance & Scalability

Unlimited scalability is possible through Apache Spark based Big Data clusters with inherent fault tolerance built-in.

Built-in Auditing

An auditing database and associated reports for usage, load and other analysis helps greatly to maintain a clean and lean system.

Implementation Process

The customer decided to utilize InetSoft's service team to work with business analysts directly. This was a close relationship where our engineers collaborated with business analysts online sharing the same environment. Project planning, management and implementation was jointly administrated. The project went through the following stages:

Status Survey

Understanding transactional database schema and mapping existing reports to the data. Besides gathering knowledge, many existing reports were identified as suspect of being one-use leftover reports.

Gap Analysis

Identifying business need gaps that are not served by current systems. The main focus in this project was to review the reporting request history. It yielded a self-service tool gap and issues related to the lack of data reusability.

Bottleneck Verification

This process helped us discover that shared transactional databases are not the only culprit. Transcontinental data latency also played a significant role that needed to be addressed besides data processing.

Consolidation Proposal

Identifying opportunities to consolidate existing reports and new requirements to reduce needed reports. For the existing static reporting system, many clusters of 5-10 reports could be consolidated into one interactive dashboard.

Architecture Adjustment

Armed with new discovery, InetSoft proposed the best way to adjust the structure. In this case, the client was offered options for public cloud based or on-premises deployment as well as the selection of location for the server.

Incremental Migration

The most in-demand reports identified in previous stages were first addressed to test the architecture with real world data for performance.

Full Implementation

Multiple InetSoft engineers were engaged. This includes designers, test engineers, and system engineers.

Performance Tuning and Monitoring

After implementation, InetSoft's engineering team kept monitoring the system looking for bottleneck and hotspots.

Training and Transitioning

At this stage, InetSoft trained the bank's IT staff to manage the system. Further training was provided to the trainer on how to teach self-service for various users in a train-the-trainer model.

Please note this is not a linear process, it took many quick iterations among the various stages.

The Results

This solution allowed the bank to increase their client base quickly without concern over system limits. Provisioning the service and enhance the system has now become a simple task. Clients not only gained vastly improved performance but also gained self-service ability. Once the system was fully deployed within 6 months, the group only needed one part time administrator/designer. The InetSoft solution and services reduced the number of IT staffing and business resources.

Copyright © 2024, InetSoft Technology Corp.

Analytics in banking: Time to realize the value

Consider three recent examples of the power of analytics in banking:

• To counter a shrinking customer base, a European bank tried a number of retention techniques focusing on inactive customers, but without significant results. Then it turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the bank. This new understanding gave rise to a targeted campaign that reduced churn by 15 percent.
• A US bank used machine learning to study the discounts its private bankers were offering to customers. Bankers claimed that they offered them only to valuable ones and more than made up for them with other, high-margin business. The analytics showed something different: patterns of unnecessary discounts that could easily be corrected. After the unit adopted the changes, revenues rose by 8 percent within a few months.
• A top consumer bank in Asia enjoyed a large market share but lagged behind its competitors in products per customer. It used advanced analytics to explore several sets of big data: customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data. The bank discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.

Results like these are the good news about analytics. But they are also the bad news. While many such projects generate eye-popping returns on investment, banks find it difficult to scale them up; the financial impact from even several great analytics efforts is often insignificant for the enterprise P&L. Some executives are even concluding that while analytics may be a welcome addition to certain activities, the difficulties in scaling it up mean that, at best, it will be only a sideline to the traditional businesses of financing, investments, and transactions and payments.

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In our view, that’s shortsighted. Analytics can involve much more than just a set of discrete projects. If banks put their considerable strategic and organizational muscle into analytics , it can and should become a true business discipline. Business leaders today may only faintly remember what banking was like before marketing and sales, for example, became a business discipline, sometime in the 1970s. They can more easily recall the days when information technology was just six guys in the basement with an IBM mainframe. A look around banks today—at all the businesses and processes powered by extraordinary IT—is a strong reminder of the way a new discipline can radically reshape the old patterns of work. Analytics has that potential.

Would you like to learn more about our Financial Services Practice ?

Why? Three factors are coming together to kick off the coming heyday. First, consider advances in technologies . The availability of information is booming: in the past few years, the amount of meaningful data—true signal, not noise—has grown exponentially, while the size and cost of processors decreased. By 2020, about 1.7 megabytes a second of new information will be created for every human being on the planet. Businesses have opened their minds, freely adapting new analytical techniques that in the past might have been dismissed as too impractical and theoretical for the real world.

And those techniques have improved radically. We are well past simple linear regressions—machine learning now features support vector machines, random forests, gradient boosting, and many other astonishing algorithms. Any company’s ability to perform these analytics has been significantly boosted by the exponential increase of  computing power (which makes it possible to undertake, in just seconds, an analysis that in the past would have taken weeks) and by new data-storage technologies, such as Hadoop.

Second, banks in many regions are under enormous economic pressure . Our latest research finds that of the top 500 institutions around the world, 54 percent are priced below book value. In 2014 we calculated that just 18 percent of banks captured all the value in the industry. Recognizing this reality, banks have tried all manner of improvements, especially digitization and cost cutting. But these moves have taken them only so far; something new is needed.

Those digitization efforts underlie the third factor pushing analytics. Much of a typical bank is now digitized and throwing off data by the terabyte. A mostly manual bank would have serious difficulty using advanced analytics; at digital banks, the highways are already paved.

Put it all together, and you get advanced analytics: industrial-scale solutions to exploit data for authentic business insights and vastly improved decision making. The tools are there; banks must now carry them forward into actions that can drive meaningful change. The canvas is as broad as a bank itself. Rich real-time data—numbers, yes, but also text, voice, and images—now exist for literally every action that customers make, every product that banks sell, and every process that banks use to deliver those products. In this article, we will explore the vast opportunities, as well as the problems of integration and scaling that keep banks from making analytics  a coherent discipline. We will then suggest the strategic and organizational elements that banks need to realize the analytics dream.

Ready for prime time

As the saying goes, “The future is already here. It’s just not evenly distributed.” Banks—and companies in every other industry—are already deploying advanced analytics to move their businesses forward. We see three ways it can generate a meaningful increase in profits (Exhibit 1).

• Advanced analytics can help banks wring small improvements out of almost all their everyday activities, boosting the traditional P&L levers. Potential moves include the following:
• Accelerating growth, even in an anemic environment. Deeper and more detailed profiles of customers, together with transactional and trading analytics, can improve the acquisition and retention of clients, as well as cross- and upselling. For example, one bank used credit-card transactional data (from both its own terminals and those of other banks) to develop offers that gave customers incentives to make regular purchases from one of the bank’s merchants. This boosted the bank’s commissions, added revenue for its merchants, and provided more value to the customer.
• Enhancing productivity. Every banking process can become faster and more effective. Among other things, banks can use advanced analytics to provide faster and more accurate responses to regulatory requests and give teams analytics-enhanced decision support. One bank we know used machine learning to understand the way the characteristics of code affected a mainframe’s running time and the resulting costs; by optimizing the code, it cut them by 15 percent. Another bank used new algorithms to predict the cash required at each of its ATMs across the country, combining this with route-optimization techniques to save money.
• Improving risk control. Banks can lower their risk costs through analytics-aided techniques, such as digital credit assessment, advanced early-warning systems, next-generation stress testing, and credit-collection analytics. The expense of compliance and control has soared in recent years, and banks can use analytics to get economic returns from their considerable investments. We estimate that G-SIBs can take out up to $1 billion a year in costs through a simplified portfolio of data repositories—building on work that most banks have already done—and through new analytics that produce more accurate regulatory reports and deliver them more quickly. D-SIBs can save up to $400 million annually. Further out, banks will be able to use analytics to reduce fraud losses.
• A second vector of impact is the way that analytics can help deliver the promise of digital banks and offer a much better customer experience at a fraction of the current cost. In some regions up to 65 percent of customers now interact with their banks via multiple channels. Their paths through them are extraordinarily complex: they often start in one channel, perform intermediate steps in others, and finish in yet another—with plenty of pauses and information-gathering loops along the way.  Successful digital banks deliver a truly seamless multichannel experience by gathering real-time data and using analytics to understand the customer and build the proper (and always consistent) journey view.
• Finally, analytics can help banks find new sources of growth , and even new business models. Banks may be able to reap income from their data—for example, by sharing customer-analytics capabilities with new ecosystem partners, such as telecom companies or retailers. Taken to a logical but not implausible extreme, banks can use data and analytics to shape a new business model and out-fintech the fintechs. The bank as data company can sit at the center of a consumer ecosystem where the revenue pools include not just banking but also many other B2C and B2B businesses. Great analytics isn’t the only requirement here: banks must get many other things right to be relevant to and trusted by customers. But that can be done, and already more than a dozen leading banks are taking positive steps in this direction.

Not without pitfalls

In our recent survey, 1 1. We interviewed executives at 13 global and regional banks based in ten countries across Europe and the Middle East. we found that almost every bank lists advanced analytics among its top five priorities. Most plan to invest further in these techniques. A few banks are already seeing the rewards. These leaders have built substantial foundations by establishing data lakes and centers of excellence and using machine-learning techniques. They and many others have spent hundreds of millions on their data (especially risk data) and on compliance. For them, advanced analytics is becoming a reflex action, with commensurate rewards of about €300 million in additional annual profit, on average.

A digital crack in banking’s business model

Most banks we surveyed, however, are struggling. A good many are “started but stuck”: they have invested significantly in data infrastructure (mostly as a result of regulation) and experimented with advanced-analytics techniques (mostly through specialized teams loosely connected to the corporate center). But the expected results have not arrived. A few banks have yet to begin.

The started-but-stuck ones are running into a number of problems. At the highest level, their efforts remain unconnected and subscale; they have not yet tied together their disparate efforts into a single, unified business discipline. Tactically, we see banks making unforced errors such as these:

• not quantifying the potential of analytics at a detailed level
• not engaging business leaders early and to develop models that really solve their problems and that they trust and will use—not a “black box”
• falling into the “pilot trap”: continually trying new experiments but not following through by fully industrializing and adopting them
• investing too much up front in data infrastructure and data quality, without a clear view of the planned use or the expected returns
• not seeking cooperation from businesses that protect rather than share their data
• undershooting the potential—some banks just put a technical infrastructure in place and hire some data scientists, and then execute analytics on a project-by-project basis
• not asking the right questions, so algorithms don’t deliver actionable insights

Making it happen

A quick take on the cio agenda.

As noted, analytics does not necessarily require a big investment in IT. But banks must provide the technologies and tools that businesses need to access an immense set of high-quality data in real time. That data must be well managed and always available. These technologies include the following:

• Data lakes to scale up and industrialize the development and delivery of use cases. There are good and bad ways to use data lakes; we’ve seen better results when they are developed with agile techniques based on use cases. At all costs, banks should avoid building another enterprise data warehouse or asking data lakes to do more than they really can. Data lakes will not unburden the banks from proper governance and quality-assurance processes, but they do offer an opportunity to get things right from the beginning.
• Machine-learning techniques, which have proved superior in many use cases—the industry will inevitably move toward machine learning supported by open-source languages. Machine-learning tools are already available in most banks but are not used to solve very specific problems. (If they are not available, there is a large market of rapidly moving open-source technologies and commercial software products.) Banks need to keep their options open to the vast innovations taking place in machine learning.
• Google-like search engines provide quick and easy access to all of a bank’s data, including the source, definition, and all other information needed to use the data effectively.
• Modern data-exploration and -visualization tools are helpful to bridge the gap between advanced business users identifying potential use cases, anomalies, or patterns in the data, on the one hand, and data scientists using advanced analytics to predict and optimize, on the other.
• Tools to analyze text, voice, video, and images can help to process new data structures—specifically, unstructured data, such as voice and text. For example, analyzing live-chat data has great business potential for retaining customers or for next-best-product-to-buy analyses.
• Capabilities to leverage real-time data are important. Deploying analytical models in live streams can help embed analytical advice and predictions into customer dialogues. For example, most banks have already added chat capabilities to call centers. Now, banks can add analytics-driven recommendations to the APIs that support chat to insert a “next best” suggestion to customers.

Avoiding the pitfalls and accessing the broad set of opportunities requires CEO leadership as banks develop two assets: a strategy for the transformation and a robust analytics organization to assist and empower the businesses as they learn to use analytics in their everyday work. (New technologies and tools are also necessary; see sidebar, “A quick take on the CIO agenda.”)

Readers may notice something that’s missing from this list: setting the aspiration. That’s because we think every institution, unless its circumstances are extraordinary, should set the same aspiration: to establish analytics as a business discipline—the go-to tool for the thousands of decision makers across the bank. Earlier we mentioned analytics as a reflex. To extend the metaphor, analytics should resemble the human nervous system; every part of the body knows what to do when presented with certain stimuli. The big difference among banks will probably be the pace at which they can build and train their systems. Just as some parts of your brain are trained and some are not, banks will find that some nerve paths are already working well, while others must be laid down and taught how to react.

There’s something else missing from our list of required assets: an additional $100 million or so of spending. Many bank leaders look at analytics and fear an outsized investment. That’s not unreasonable, since in recent years institutions have had to spend billions on things they could not have anticipated, and budgets are very thin. But analytics is not a bet-the-bank investment with no graceful exits; it’s a short-cycle flow of investments with lots of options to kill unsuccessful pilots. The small but immediate payoffs from the initial work can finance the next wave of projects, which in turn finance more and larger efforts. Once the system is built, the investment is over and the margins become enormous—like those of software or tech companies.

A clear strategy centered on high-priority applications

Three elements are essential to the strategy. First, banks need an analytics-ready mind-set . Analytics transforms everyday work in surprising ways, so leaders must open their minds to the possibilities. Our core beliefs about advanced analytics can help. 2 2. For more, see Helen Mayhew, Tamim Saleh, and Simon Williams, “ Making data analytics work for you—instead of the other way around ,” McKinsey Quarterly , October 2016.

• Great analytics starts with high-value questions, not data. To guide the discovery process, ask what problem you want to solve and how much value the solution can create. Do not launch yourself into analysis for the sake of analysis or into intellectually interesting problems whose solutions are not actionable.
• The smallest edge can make the biggest difference. Advanced analytics is not about solving your biggest problems; it’s about solving hundreds of small ones that all add up. Especially in operations, these techniques can help to redefine processes and shorten them by several steps.
• Insights live at the boundaries between data sets. Remember the bank that combined six or seven discrete data sets to build a tool predicting the next product to buy? It realized that lots of relationships become apparent only when it compared widely varying parameters. Banks have massive amounts of data scattered through different departments. Pilots to bring together small samples of information can reveal the potential.
• Loops beat lines every time. Following a process can be a slog, and to do so you sometimes have to put on blinders. But that mentality is exactly wrong for advanced analytics. Banks that use feedback loops can not only be faster to market than competitors but also arrive there with better products. Ultimately, machines learn just as we do: by trial and error.
• Design matters. You want people to use your new tools. A beautiful algorithm deserves an attractive package that catches the eye of your users. Most of them can’t read code or understand the output of a model. To act on these insights, they need easily readied and used dashboards that help them make decisions and test potential scenarios.
• Analytics isn’t enough; adoption is essential. Use whatever means necessary—incentives, role modeling, communication, more communication—to get decision makers to use the new tools. Way too often, best-in-class algorithms sit idle in computers because users do not trust what they regard as a black box, fear the impact it could have on their roles, or simply do not want to go through the discomfort of change. Creating analytics is like putting jet fuel in your car. At the end of the day, if the driver does not develop the skills needed to drive faster, the effort is wasted.
• Analytics is a team sport. The skills banks need to make analytics work cannot be contained within a single person—at least not yet. Your teams must include true experts on data science, engineering, data architecture, and design. Faking it with people who do a little bit of everything won’t work.

A second element of the strategy is a set of prioritized use cases and a mechanism to create a pipeline of them. The scope for analytics is vast. Anywhere a bank uses rules of thumb or something is done “the way we’ve always done it,” analytics can probably make improvements. The CEO must lead the hunt for these issues and help prioritize them. Critically, at the beginning, the chosen use cases should not be limited to applications in which analytics could produce a substantial uptick in results; they should also include areas where scale can be increased quickly, to avoid the “pilot trap.”

Most of the potential use cases are relevant to every banking business. They include commercial applications: cross-selling and upselling, customer acquisition, reducing churn, and winning back customers. Business-improvement levers (such as dynamic and value pricing, credit underwriting, sales-area planning, yield and claims management, fraud detection, call-center routing, and workforce planning) are also relevant for most banks. While the first couple of use cases can be introduced top-down or outside-in, it is just as important to encourage everybody in the bank to become creative and make suggestions—while always ensuring a clear path to creating value. To avoid discouragement, long validation and delivery cycles need to be shortened. Innovation labs can help accelerate the process.

Finally, a strategy should set out a vision for how the bank will use analytics applications. For each use case the bank is considering, it should start by asking what problem holds back the business from having a greater impact. It can then work through a set of five steps: identifying the source of value, considering the available data (easier to do with a data lake, as we describe in the sidebar), identifying the analytics technique that will respond to the problem and probably produce insights, considering how to integrate analytics into the workflow of the business, and anticipating the problems of adoption (Exhibit 2).

Will sellers use the tools? If not, why not? What are their needs, and how can you make the analytical tools responsive to them? Sometimes the answer involves bundling insights from algorithms with useful data for sales managers in an app that they can use on external visits. Other times, a bank will have to change the way it develops campaigns and pushes them to the front line and to customers. Finally, in many other cases the bank will have to develop a group of high-performing champions who embrace this discipline and act as role models.

Embedding analytics in the organization is not simply a matter of getting specific teams to use specific tools, though that’s essential. The CEO and the top team must do much more to communicate clearly that analytics is important to the bank and empower everyone to join the revolution. The  classical steps of successful change management will be essential: role modeling the new behavior, clearly explaining why change is needed, building the skills of the businesses so they can succeed with the new tools, and reinforcing the bank’s commitment through formal mechanisms (such as incentives).

A powerful analytics network

The businesses will need help to design analytics systems, to build applications exploiting them, and to promote adoption. Banks will want to establish a central team that supports these needs. But the last thing they should do is build another silo. What they require is a networked structure, a kind of nervous system. More than anything, banks must have open channels and accessibility to make a knowledge of analytics pass freely throughout the enterprise. An analytics center of excellence, the spine of such a system, will probably need some or all of the following components:

• New roles and responsibilities in data management, advanced analytics, and campaign execution. Perhaps the most critical role is that of a chief data officer responsible not only for analytics strategy and its integration into business units but also for defining data management’s roles and responsibilities, monitoring the quality of data, and ensuring regulatory compliance.
• Imaginative channels, such as innovation labs, to bring analytics closer to users.
• A deep pipeline of analytics talent, cited as a top priority by 60 percent of the banks in our recent survey. Banks should typically start with small teams of data scientists, who can work with external partners to absorb the necessary competences and skills, and then scale up gradually.
• A clear governance plan with a strong center of excellence (headed by the chief data officer) and well-defined responsibilities for the governance and quality of data and for the execution of analytics within the businesses.
• “Gold standard” data-management processes that define clear accountability, maintain the quality of data, and manage metadata, the data life cycle, and controls.
• Bulletproof data-quality controls, starting at the very beginning of the data life cycle. They should be automated wherever possible and monitored by a set of key quality indicators, so that the businesses are accountable for collecting the right data.

More than 90 percent of the top 50 banks around the world are using advanced analytics. Most are having one-off successes but can’t scale up. Nonetheless, some leaders are emerging. Such banks invest in talent through graduate programs. They partner with firms that specialize in analytics and have committed themselves to making strategic investments to bolster their analytics capabilities. Within a couple of years, these leaders may be able develop a critical advantage. Where they go, others must follow—and the sooner the better because success will come, more than anything else, from real-world experience.

Amit Garg is a partner in McKinsey’s New York office, Davide Grande is a partner in the Milan office, Gloria Macías-Lizaso Miranda is a partner in the Madrid office, and Christoph Sporleder is a senior expert in the Frankfurt office, where Eckart Windhagen is a senior partner.

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Exploring Artificial Intelligence Adoption in the Banking Sector: Multiple Case Studies

• First Online: 16 October 2024

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• Maryam Adhaen 9 ,
• Weifeng Chen 9 ,
• Rami Abu Wadi 9 , 10 &
• Esra Aldhaen 10  

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We currently inhabit an era dominated by intelligent machines, where AI has seamlessly integrated into our daily lives, manifesting its impact in both subtle and conspicuous ways. In the banking sector, AI is being harnessed to execute transactions, identify potential security threats using facial recognition technologies—as outlined by the Harvard journal in 2016—combat fraudulent activities, enhance customer responses, provide personalized customer service, and enable virtual assistants to deliver real-time solutions. Moreover, AI facilitates digital documentation, problem-solving, planning, reasoning, and learning within the banking industry which leads to achieve sustainable increase in profits and value in this new era. It is important once adopt AI is to create an AI framework, design a targeted application approach, implement and construction and supervise and refine. The research addresses the AI adoption in banks, knows the bank redness of the AI implementation, enables to develop, and implement AI in its business model which shall happen after developing its organizational change and its efficiency to achieve completive advantages.

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Adhaen, M., Chen, W., Wadi, R.A., Aldhaen, E. (2024). Exploring Artificial Intelligence Adoption in the Banking Sector: Multiple Case Studies. In: Hamdan, A., Braendle, U. (eds) Harnessing AI, Machine Learning, and IoT for Intelligent Business. Studies in Systems, Decision and Control, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-031-66218-8_23

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Business Intelligence in Banking – Current Applications

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Decision-makers in the banking sector have a unique set of business intelligence needs, and artificial intelligence has been on the radar of banking executives for several years now. It follows that AI and machine learning would find their way into business intelligence applications for the banking sector.

In this report, we discuss AI-driven business intelligence software for the banking sector and their evidence of success. As it turns out, there isn’t much. The companies covered in this report all lack banking case studies for their business intelligence software, which may be indicative of the nascency of AI for this particular application in banking. That said, these companies do offer case studies for other sectors.

Nevertheless, in general, there seem to be two ways in which AI-driven business intelligence software could bring value to banks and financial institutions , including investment firms . These include:

• Report Generation   – creating reports with a variety of visualizations that suit the needs of employees at different departments
• Predictive Analytics – correlating enterprise data to find patterns on which executives can take action

In this report, we’ll discuss the uses cases of AI-driven business intelligence applications in the banking sector by taking a look at four vendors that offer AI software to banks. We’ll begin by examining the vendors offering report generation-related AI solutions:

Report Generation

SAS offers software called SAS Visual Analytics , which it claims can help banks provide their lead staff with self-service analytics and interactive reports using what appears to be a combination of predictive analytics and natural language processing . For banks, this analysis and reporting may be related to customer buying patterns, loan payments , or customer experience .

This could be for strategies on gaining customers or to finding customers less likely to default on loans. SAS claims that in addition to the data analytics capabilities of its solution, it can also discern the sentiment behind text data, such as social media posts, and mark it as positive or negative. This is likely accomplished through natural language processing.

Below is an 8 -minute video demonstrating how SAS Visual Analytics can segment customers. In this video, the demonstrator is able to segment the section of his customer base by 4:40:

We can infer the machine learning model behind the predictive analytics portion of the software needs to be trained on the client’s data related to banking transactions, customer profiles, and geolocation. For example, if a customer made an ATM withdrawal, the relevant data from that transaction would be the customer’s age, what gender they are, and where the ATM is relative to their bank. Customer segments could become more specific than this, however. The data would then be run through the software’s machine learning algorithm.

This would have trained the algorithm to discern which data points correlate to changes in behavior or new patterns forming within a bank’s customer base. This could be across the entire customer base or focused into a segment of customers based on age, gender, or geolocation .

The software would then be able to predict, report on, and visualize these changes or patterns. As a result, business leaders can provide organized information to other departments. Users can create various types of graphs and charts to depict the data in a more organized fashion, and these models can be shared between staff through the software. This may or may not require the user to upload information about their future plans or banking protocol into the software beforehand.

If the SAS Visual Analytics solution does leverage natural language processing to analyze text data, it would have to be trained on thousands of social media posts or other text data samples, such as emails. This data would involve customer experiences in financial aspects such as banking and loans. The data would be labeled as a negative or positive experience.

SAS would then expose the machine learning model to the labeled text data. This would train the algorithm to discern the chains of text that, to the human brain, might be interpreted as a positive or negative sentiment as conveyed by a social media post.

SAS Visual Analytics would then be able to run through public social media posts or any other text data in need of sentiment analysis, and the software’s algorithm would be able to determine if the customer’s experience was positive or negative. This may require the users to upload data about their future changes to banking operations into the software beforehand.

It’s likely that users in banking can integrate the software into existing databases for banking and customer information, as well as systems that would allow the transfer of text data from social media.

SAS does not make available any case studies showing a bank or financial institution’s success with their software, but it lists  Royal Bank of Scotland, Cosmos Bank, and Erste Bank Croatia as some of their past clients.

Jim Goodnight is CEO at SAS . He holds a PhD in Statistics from North Carolina State University . Goodnight has spent the last 42 years of his career at SAS.

Thoughtspot

Thoughtspot offers software called Thoughtspot Embedded Analytics , which it claims can help banks and financial institutions easily search through large stores of data. A user can purportedly start typing in a search term into the software’s search function, such as “New York,” and the search bar creates a drop-down menu of possible insights the user may want to generate a graph out of.

The software also purportedly allows users to type questions into the search bar using everyday language, which likely indicates that the system uses natural language processing. The company claims that the insights its software generates for users are powered by machine learning. Generating correlations out of large volumes of data is a classic use case for machine learning.

The software starts generating a graph before the user has selected the kind of visualization that they prefer at the moment. It is unclear if the graph is generated based on the user’s own history of chosen visualizations or if there is a default of some sort.

The video below demonstrates the software’s search function and shows how graphs are generated as the user types:

For example, a user might want to figure out if a certain age demographic, say millennials, were more or less likely to default on their loans within a given location, say New York. They could type relevant search terms (millennials, New York, loan default) into the search function and be presented with several options for visualizing several different correlations, including one between millennials, New York, and loan default status.

Thoughtspot claims banks  can integrate their stores of data into Thoughtspot within a relatively short timeframe in comparison to other data visualization tools, although they do not present a case study to verify this claim.

Thoughtspot does not make available any case studies that show a bank’s success with the software, but they list   Scotiabank and Sterling National Bank as some of their past clients.

Amit Prakash is co-founder and CTO at Thoughtspot . He holds a PhD in Computer Engineering from The University of Texas at Austin . Previously, Prakash served as Software Engineer at Microsoft .

Aithent offers software called Aithent Analytics for Business Intelligence (AABI) , which it claims can help banks and financial institutions transform data from multiple and disparate sources into actionable knowledge for their management using predictive analytics . Such knowledge involves profit and loss, return on investment, and customer information.

The software is reported to create templates from detected patterns to help business leaders in their decision making. The templates serve as visualizations of current trends, which is helpful in communicating technical changes to other departments and for planning ahead. Aithent claims their software’s reporting and templating of data can be scaled widely or acutely to find insights for more specific business areas.

In addition, they advertise the ability for users to generate custom alerts for patterns they want to monitor closely. With this feature, users can eliminate false positive alerts by indicating to the software that the alert was not helpful or did not accurately reflect what is likely to happen.

The company states their software’s machine learning model would need to be trained on their client’s own company data, which likely consists of transaction data, customer profiles, and loan contracts. Aithent would then expose the machine learning algorithm behind their software to the client’s data.

This would train the algorithm to discern which data points correlate to patterns in customer banking or buying behavior, such as a customer who has defaulted on multiple loans within a certain period. The algorithm could also discern the data that correlates to customer behavior changes across segments, as well as fraud or attempts at fraud.

The software could then predict and report on various customer patterns a bank manager would want to know about when making decisions for their company. These could include behavioral patterns that usually precede fraud and customers with a high risk of defaulting on a loan.

These patterns can also be found within specific customer segments, such as men aged 30-50 years old. This may or may not require the user to upload information about their recent loan contracts or future business plans into the software beforehand.

Aithent claims bank managers can integrate the software into existing databases for bank and customer information.

We could not find a video demonstrating how Aithent’s AABI software works.  Aithent does not make available any case studies showing success with their software, and they do not list any major banks as clients for this solution.

Gourish Hosangady is Executive Vice President of Business Analytics at DataRobot . He holds an MS in Engineering from the University of Missouri . Previously, Hosangady served as Vice President of Business Intelligence Solutions at Inforeem .

Predictive Analytics

DataRobot offers a namesake software which it claims can help banks and financial institutions gain insights from business data and create tables and models from company data. Each predictive model is a visualized representation of the predictions the software makes to organize the results for clarity and communicability. They do this using predictive analytics .

DataRobot states their software’s machine learning model is trained on their client’s company data, which is likely hundreds of thousands of customer profiles and transaction records. For example, a predictive model could display all of a given customer’s late payments and loan defaults in a visualized chart by accessing the banking data associated with that customer. To do this, the software would need to have been trained on loan and customer data relating to prepayment risk, credit risk, and late payments.

The bank or data scientist would then expose the machine learning algorithm to this data. This would have trained the algorithm to discern which data points correlate to the desired search term or terms that narrow down data results and could be used to create a predictive model in the form of a graph or table.

For our example, this would be a search term such as “will_default” as defined by a bank or data scientist. The underscore appears to be used to keep the search term recognized as a single term, and not as two distinct terms.

The software would then be able to make the user’s requested prediction and create a visualization such as a graph, spreadsheet, or table with the data that results from a search term or set of search terms.

This could be in the form of a table or chart listing each potential customer’s likelihood to default on their requested loan. This may or may not require the user to use data about their recent or future loan contracts into the software beforehand.

DataRobot claims banks and financial institutions can integrate the software into their existing databases and other stores of company data.

Below is a short 2 -minute video describing how DataRobot can help banks and financial institutions predict risk for lending. At 1:29, the user’s desired prediction search term is shown with an underscore.

DataRobot does not list any explicit banking case studies, but banking is one of the industries they list on their website. They also claim USBank as a past client.

Tom de Godoy is c o-founder and CTO at DataRobot. He holds an MS in Mathematics from UMass Lowell . Previously, Godoy served as Senior Director of Research and Modeling at Traveler’s Insurance .

Header Image Credit: Nairobi Business Monthly

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Practical AI-informed decisioning in the bank

SAS’s Stephen Greer joins the BAI Banking Strategies podcast to offer his insight on how fast-emerging generative AI can improve decisioning at your financial institution.

Key takeaways: Reasonable expectations: AI-based decisioning in financial services leverages your existing tools and complements human judgment and intuition.

A task-based approach: Pinpoint the business challenge at hand and minimize risk with targeted AI-informed decisioning, such as pulling up AI-powered next-best product recommendations.

We’ve been here before: SAS’s Stephen Greer compares the mixed emotions around AI to the early days of cloud adoption. Benefits of cloud data storage were clear, but there was apprehension about security and other factors from major institutions.

Rachel Koning Beals : Whether your institution is an adopter or just an explorer, AI-informed decisioning in banking has arrived.

SAS’s Stephen Greer joins me, BAI Senior Editor Rachel Koning Beals, on the BAI Banking Strategies podcast to tell us how AI-informed decisioning is changing the bank as we know it. Let’s go deeper.

Stephen Greer is an industry consultant and financial services strategist with data analytics and AI solutions provider SAS. Over his career, he has developed a passion for helping banks and credit unions succeed at the intersection of technology and business strategy. He believes data and AI can be smartly and safely harnessed to solve industry challenges, maximize customer relationships and grow revenue. Welcome, Stephen.

Stephen Greer : Thank you for having me on.

Rachel Koning Beals : You bet. Stephen, you and I find ourselves connecting at a time that data informed banking, machine learning, AI, and especially generative AI, bring a lot of change, or at least the potential for change to financial services, and very quickly, with major market shifts even in just the past year or so. From the SAS vantage point, what’s changed the most and where do we as an industry sit today with these developments?

Stephen Greer : Yeah, I think that’s a really good question, and I’d almost say in a weird way, what’s changed the most, is we’re talking about just the last year to year and a half, is really the expectation for the future. And what I mean by that is that you had AI go mainstream in 2022 with ChatGPT. It’s almost been treated like a silver bullet that we’ve seen compared to other types of AI and predictive models. It’s the most user friendly. You can apply it to all these different problem areas. So what we’ve seen is this rush to launch a variety of different pilots, but in that process, there’s been this kind of slow realization that there’s a lot of potential here and some very clear productivity benefits. But you still have a lot of inherent limitations. And so we’re almost starting to see a reassessment of where these models are best suited, and kind of where some more traditional forms of AI might be a better option. So we’re seeing a resurgence of interest in AI, and particularly how you integrate that AI into operations for decisioning, and also a recognition that how we talk about it might change a little bit. We might try to step back and think about AI-based decisioning in a broader context, which takes all the tools into account that you might use to solve for various industry challenges.

Rachel Koning Beals: You started to talk a little bit about what we mean by AI-informed decisioning. But can we sum it up in a definition?

Stephen Greer: Yeah, definitely. So everyone wants to be an AI-driven organization, which means informing operational decision making. So if you think about it, every decision in financial services is the output of some combination of predictive models, decisioning, rules, human judgment and reasoning. So just like an algorithm on a computer, these decisions work by starting with an input and then ending with an output, and then analysis that takes place in between. And so sometimes you have that in between that is very simple and explainable. Sometimes it’s extremely complex, right? It could be mostly or all human. It could be entirely machine. And so when we think about this, institutions have so much input data, which holds the insights to enable better decisions, and the processing that happens in between then is how those insights are extracted and unlocked. And so when we think about AI-based decisioning, we’re thinking about using AI to gain considerable leverage, which complements human judgment and intuition.

So at SAS, our definition in financial services, AI then becomes essentially a system to support and accelerate human decisions and actions. You often hear us talk about this in terms of human in, on or out of the loop, depending on the decision or depending on the complexity of it.

Rachel Koning Beals: I hear the word automation, or at least the promise, sort of the hype of automation around this technology. True?

Stephen Greer: What we’ve seen be successful in financial services is just acknowledging that the complexity of putting AI into a decision flow. So oftentimes, we aren’t really advocating for full autonomy. We’re advocating for leverage, and usually with some of the more advanced models, like generative AI, right? That’s usually a human in or on the loop. And so this leverage, to kind of allow the employee to do more high-value work and offload some of the more monotonous or rote work that takes so much time and can add friction and be an impediment to getting a model or getting advanced analytics into a production state. And so when we talk about automation, that’s kind of how we view it, where we’ve seen institutions be successful building on that.

Rachel Koning Beals: Are there some more obvious departments in the bank or credit union where AI decisioning makes the most sense?

Stephen Greer : Yeah, it’s kind of everywhere. Maybe it’s worth putting in context what we mean when we talk about AI. One thing we’ve seen, that we tend to gravitate towards and where the hype is right now, that’s generative AI. It’s kind of what everyone wants to talk about. It’s what you’re seeing in the news, and it’s kind of when you think of AI, you think of what’s the latest technology that’s been released? But AI is a broad umbrella, right? And there are a lot of different disciplines, and there are a lot of different techniques that are underneath that, and each of those techniques are optimized to solve for specific problem sets.

So maybe we could put it into maybe the context of an onboarding journey. So let’s say you start with marketing automation. You’re calculating turn propensity or next-best action for a specific segment, and then you’re serving up an offer with AI as the underpinning for that decision. A customer might apply for that offer, and then a credit risk assessment is made using AI, right? Decisions are made on the pricing and the loan amount behind the scenes, or what the offer is that you’re going to be giving that specific customer. After that, you have things like customer due diligence and other fraud checks that require decisioning, the sanction screening, KYC requirements. Is this application fraud? Do we need to trigger additional controls here? Then you’ve got after-funding and provisioning. You’ve got the ongoing servicing and monitoring, so protecting early warnings for maybe a stressed borrower, and deciding on different treatments for collections if they go into default and delinquency, and onto all the authorizing upsells, credit line increases, ongoing transaction monitoring. So just within that journey itself, you can see all the different areas in which AI could play a part and where you could amplify human-based decisioning through advanced AI models.

Rachel Koning Beals : In a way with that onboarding example, you’ve sort of answered this, but it does make me wonder, can this migration, this adoption of AI often be task based, or sort of product specific? Or should banks think more whole-of-enterprise when they’re approaching AI?

Stephen Greer: Yeah. I mean, it kind of depends on the organization. We’re seeing a lot of task-based approaches that might look at a discrete process and then explore ways to optimize it, and so you can get the benefit, and this lowers risks. It might be quicker to achieve. It might get closer to the business problem, which, in the case of a lot of generative models, for instance, like LLMs (large-language models), is increasingly where they’re doing the best. Then you have kind of the constraint of getting closer to the business problem itself, rather than some kind of foundational model that can boil the ocean. And so in that instance, task-based approaches more specific to a process make the most sense.

But then we also have a lot of institutions undergoing larger transformations, where we see a couple of our customers have consolidated decision engines, right, or consolidated systems into a single decision engine. And so this might create a more cohesive approach, where you get some scalability benefits. You might have more governance and control over the outcome of how AI is being used across a variety of different lines of business or processes. And so it kind of varies depending on who you’re talking about and what kind of problem they’re trying to solve.

Rachel Koning Beals: You know, we’re sort of in early days here, especially with Gen AI. Have you guys as consultants already been able to help clients deal with some pain points with pivots? Because, for instance, what they’ve market tested has changed minds. I’m interested in knowing, are there sort of sophomore year market tests going on out there? What stage are we at?

Stephen Greer:   In some ways, depending on how familiar they are with the space, there’s pretty well-established challenges that we have today, and you deal with hallucinations – that’s a big one. Output performance and the size of the context window, or the input data kind of requires some pre-processing and other things that go into it. There are concerns around interpretability and explainability of the results, whether or not bias is being mitigated. Regulators are especially concerned here. There are all these concerns around cost and speed of the different models … which might limit their scalability or use in kind of real-time context. I would say, one of the biggest pain points with these models, you kind of have this creativity that we aren’t used to, and a new type of problem set that they’re trying to solve for. So if you think about like traditional machine learning or traditional AI, usually it’s to solve a specific task where the output is fairly well defined. What your goal is, what you want to predict, is fairly well defined. But with generative models, you have a different approach, where they’re learning the distribution of the data it’s trained on and how it’s structured, so it can reproduce something that looks similarly structured to the data that it was trained on, but it doesn’t follow directly from it the way it would in traditional machine learning context. So you get this kind of low level judgment about the output, and it kind of appears like creativity, which is interesting for us. We aren’t really used to that kind of creativity inherent to these models, and so I think that’s impacting the expectations and how they’re being attempted to put into production.

But really, where we’re at now is very early days, a lot of really cool demos. There’s incredible potential in the future, but we aren’t seeing anything substantial yet. We aren’t seeing that kind of killer use case.

Rachel Koning Beals: I mean, that’s kind of a good segue, in a way, to your advice on which stakeholders and when to potentially get involved when pitching AI decisioning. It strikes me we are talking about potentially everything from shareholders to board members to your call center leads to your marketing heads. It’s a long list, potentially, right?

Stephen Greer: So at the end of the day, if you’re in financial services, especially a bank, you’re in the risk and trust business, and that’s a lens that you apply across everything that you do in the industry. And for good reason, right? Because you’re handling customers’ money. When we think about this space, and we think about AI-based decisioning, our posture has always been, and we think this should be the posture of the industry as a whole, has always been a reflection of the industries we operate in. And so we work with very highly regulated, highly risk focused [institutions], and they approach very complex problem solving, and the consequences are really high, right? There’s low tolerance for failure here. And so we need to trust that when we put the AI models into production, [they] are fair and ethical and that controls and governance structures are in place so that we can mitigate any unwanted or adverse impacts of model drift or bias data that might creep into the training and all of those things.

So we’ve seen in conversations like that for our clients, at the major concern a risk of putting a model in production and making sure that it’s not biased, so that it’s not drifting and causing harm to their customers. That represents a significant risk for an organization. That is always in the back of our mind. It’s in the back of the minds of our clients. And so we’ve tried, to that extent, to embed those kinds of capabilities across the life cycle, to maintain that trustworthiness and that ethical AI standpoint.

Rachel Koning Beals:  Is this an industry shift that discriminates by bank size, only for the biggest operators, or is it more of a leveler, potentially?

Stephen Greer: I think it used to be the case that when you were smaller, you were at a significant disadvantage. I mean, it definitely still is, I think as far as just having the resources, the scalability, the kind of heft to throw money at tech and to attract the talent needed to, like, build internally, and all these things that a Tier 1 might do. But I think we’re in a significant period of change here where there’s a democratization of what was previously kind of opaque processes. You had real steep learning curves for data science, for instance, data science initiatives and extensive investment requirements if you wanted to get benefit out of it. We’re at this place where the accessibility of a lot of these tools keeps getting better and has really never been better. So traditionally, right, if you’re small, you’re running up against tech constraints, but we’re seeing increasingly that these tools allow an organization to kind of punch above their weight. And for us, this is incredibly exciting, because it means that all of a sudden, you’re democratizing Innovation, and you’re gaining parity industry wide, and opening up possibilities for all of these different institutions of various sizes to now engage in an area where it might have been prohibitive 10 years ago.

Rachel Koning Beals: Any comparable tech adoption of recent memory that might help us understand all these mixed feelings around AI, anything come to mind?

Stephen Greer: I kind of think of this as… similar to cloud computing, in the early days of the cloud. When cloud computing first came onto the scene, the benefits were clear. Or at least they were easy to describe. But there was some apprehension from a lot of major institutions. You had concerns around multi-tenancy, like if a security vulnerability could cascade into, you know, another tenant. There were questions around where data was residing, how encryption took place…how you could demonstrate regulatory compliance.

I remember it was a huge issue when if you were procuring a vendor who was deployed on the cloud, a lot of the processes were seen as somewhat of a black box.

Rachel Koning Beals: We talked a little bit about automation, but listen, there’s sort of these other, bigger, existential almost, questions around AI and the future of banking, and banking is getting increasingly unique all the time. Long gone are the days really, of just walking into one branch dealing with one banker. So people ask very good questions about the future the bank, should AI replace more humans? What’s your take?

Stephen Greer: It’s a good question, and it’s something that’s been top of mind for a lot of people, thinking about this and thinking about the potential. And there’s been a lot of, I think, fear here. My take is that I think if you look at where we are today in many different domains of work – and this view is shared with a lot of people across tech – there’s demand for services which far outpace the supply. You’ve got IT teams that are overwhelmed by different feature requests. You’ve got migration efforts that are monopolizing schedules of release cycles. You think about how much COBOL still exists in financial services today, a 60-year-old language, right? Fraud investigators and compliance officers are often inundated with different cases, and the traditional response has been to just throw more bodies at the problem, rather than to increase the efficiency there when there’s kind of a regulatory requirement. [Regulation and compliance] is another area that we’ve seen a lot of issues where you kind of did the fire drill for the amount of effort required to bring an institution up to compliance, scan documents, review policies, all of these things, right? So there’s so much noise and there’s not enough signal, and there’s so much demand where there’s not enough supply.

So when I think about this technology, you really think about the leverage to offload a lot of what we’d call menial work, to spend more time with higher-value work. And ultimately we feel like this will be the outcome… will be more work going live, more models put into production, more time on things that are actually going to make a difference rather than things that, from a day-to-day basis, kind of bog you down.

Rachel Koning Beals: I think that’s a great place to stop, because it gives us all a lot to think about. We can all be better at our jobs. What does banking look like today and tomorrow? Stephen, thanks for joining us. We’d love to have you back on the BAI Banking Strategies podcast.

Stephen Greer: I enjoyed it.

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