How Banks Can Make Use of Data-Driven Customer Insight

By Mohammad Nasar and Christopher J. Sifter
4/11/2019
How Banks Can Make Use of Data-Driven Customer Insight
As competition in the financial services industry continues to intensify, banks are ramping up their use of machine learning and artificial intelligence (AI) as part of their efforts to attract and retain customers, increase customer engagement, and improve the overall customer experience.

A growing number of banks are finding that today’s advanced analytic and data intelligence capabilities can give them deeper insight into their customers’ behaviors and expectations. Armed with this understanding, they are able to do a better job developing products and services that address their customers’ needs and deliver these in a way that reinforces customer satisfaction and loyalty.

Driving the demand for greater customer insight

As new types of financial services organizations compete for their business, today’s bank customers are becoming more discerning and have heightened expectations regarding convenience and accessibility to banking services. At the same time, customers also are becoming more knowledgeable and technologically adept, demonstrating increased affinity for digital technology in general and the online management of financial transactions in particular. These trends, coupled with continuing sensitivity to fees and charges, have provided an opening for many financial technology organizations to fill a perceived gap in certain banking services.

Banks are responding by taking a hard look at how they manage the customer experience, with increasing focus on mobile and digital transactions. Meanwhile, although regulatory pressures are easing somewhat, compliance continues to demand considerable resources and add to the pressures on traditional banking organizations.

In this environment, a growing number of banks have come to regard analytics, AI, and machine learning as increasingly important tools in their efforts to operate more efficiently while still attracting and retaining customers. For example, when bank executives participating in a recent Crowe webinar were asked how they view machine learning and improved customer insight as an organizational priority, almost nine out of 10 gave the issue some degree of priority – only 11 percent said it was not a high priority.
exhibit 1
Source: Online survey of Crowe webinar participants, Sept. 20, 2018. Numbers might not add to 100 because of rounding.
Yet even as the banking industry in general appears to recognize the need for new technical capabilities to improve customer insight, many banks are still struggling to embrace the technology. Nearly half (47 percent) of the respondents in the survey expressed interest but acknowledged other pressing needs are competing for bank resources and attention. Only 23 percent of the participants said they are embracing machine learning and customer insight as part of their day-to-day business.

Data science applications in banking

Historically, business intelligence has focused on recording and reporting what happened in the past regarding a customer, an account, or various critical business metrics. The goal was to develop dashboards that helped managers visualize and understand what had happened, so they could develop strategies for improving performance.

Today’s business intelligence systems move beyond such descriptive analysis and advance to the level of predictive analysis, using advanced customer segmentation models to identify which customers are likely to repay a loan early, open or close a deposit account, use electronic banking, visit a branch, add to a credit line, or engage in various other activities. Machine learning advances the process even further into the realm of prescriptive analysis, in which the system responds by recommending actions designed to increase or decrease the likelihood of something happening.

Rather than relying on human actors to identify patterns, predict their significance, and prescribe an appropriate response, machine learning uses self-adaptive algorithms that automate the identification, prediction, and prescription processes. Successful banks are using the insights gained from AI and machine learning to develop and implement sophisticated predictive and prescriptive models that enable them to address a variety of topics such as these:
  • Customer propensity to buy. Analyzing various financial, demographic, and behavioral characteristics can help sales organizations understand the likelihood that a customer will make a purchase and thereby make better use of their resources. Some insurance organizations have pioneered the use of such segmentation techniques to prioritize how agents handle online requests for quotes, enabling them to focus on promising prospects who still need some additional attention or inducement to make a buying decision. The same capabilities offer promise in banking industry applications, such as the marketing of commercial lending, treasury management, and other premium products and services.
  • Customer lapse or churn. Applying machine learning models to a broad array of customer data can help banks predict the likelihood that a customer relationship is at risk. By understanding the internal and external drivers that are at work in the relationship, banks can interact with customers in a more meaningful way and apply their resources to those situations in which they are more likely to be able to affect the outcome. If behavior patterns suggest a customer is at risk because of a bad experience or lack of a desired product offering, machine learning can help develop a proactive intervention strategy. Conversely, when patterns indicate a customer is likely to leave because of a relocation or other external factors beyond the bank’s control, these same models will suggest that the bank’s efforts be diverted to other relationships that the bank is more able to affect.
  • Life changes. By analyzing macro indicators, such as the local business climate and noteworthy economic indicators, along with individual customer transaction patterns, social media comments, and other indicators, banks can begin to predict when major life events – either positive or negative – are likely to affect a customer’s banking relationships. This can enable the bank to fine-tune its product offerings and other account management features to each specific customer.
  • Underwriting. AI and machine learning technology can alert the bank to warning signs and inconsistencies that could indicate inaccurate data or other underwriting errors. Advanced data analytics also can streamline the entire underwriting process by automating many manual tasks while reducing the likelihood of human errors or bias creeping into the process.
  • Customer services and complaints. Advances in voice recognition and other technology make it possible to screen call center and customer service center communications to identify and confirm recurring customer service issues. One of the most important advances in this area was the development of natural language processing algorithms, which look for key words or patterns of behavior that are indicative of customer service concerns, alleviating the need for people to wade through large volumes of test or audio recordings.
  • Branch rationalization and profitability. Branch operations continue to offer many opportunities for more cost-effective decision-making. Advanced data analytic programs can import external market information as well as internal branch traffic and transaction data to identify such opportunities and recommend appropriate responses. Such analyses can pinpoint where branches are competing for the same customers and should be consolidated, as well as where branch locations are most likely to yield a positive return on investment. These studies also play a major role in evaluating merger or acquisition strategies.
  • Dynamic customer segmentation. The ability to import greater volumes of data from both internal and external sources enables more accurate and detailed customer segmentation models. Organizing the customer base into various categories based on similarities that might not have been identified in the past can open the door to significant new marketing and strategic planning opportunities. Data science technology also has many applications in the compliance field, providing advanced customer insights needed for compliance with anti-money laundering (AML) and other regulatory requirements.
  • Entity resolution. One of the fundamental capabilities in AML compliance is the ability to identify multiple transactions or related accounts in which an individual or other entity has interests. Advanced data science platforms offer the ability to look at transactions through multiple lenses, revealing similarities, counterparties, and other data patterns that would otherwise escape attention. In addition, such models help banks deploy their resources more effectively, providing both cost savings and improved compliance outcomes.
  • Price elasticity and product offering. Today’s detailed segmentation capabilities let banks tailor marketing and sales strategies more precisely to each customer. Many banks are using the insights gained from AI and machine learning to fine-tune their product offerings, in some cases unbundling product features so customers can personalize their accounts with features that are of particular value to them. On a broader scale, this technology also helps banks analyze pricing strategies, fee structures, and other account features to identify the mix of features that is most likely to appeal to the most crucial and desired customer segments.

Critical requirements for developing deeper customer insights

The foundation for developing such customer insights is the ability to access a complete 360-degree view of the customer. In the past, such a view would encompass every aspect of the customer’s relationship with the bank, but today it is merely the starting point.

In order to effectively apply AI and machine learning capabilities, banks need to move beyond a view that is strictly internally focused, and instead develop a full picture of customers’ external relationships as well. To accomplish this, banks must draw on a wide range of external information sources that enable them to understand their customers’ relationships with other financial services entities as well as other businesses, social organizations, and networks.

Making reliable predictions that drive sound decision-making requires data that is dependable, consistent, and readily accessible. This is where the issue of data governance becomes vital. The objective of data governance is to see to it that data is managed as a business-critical organizational asset, comparable to an organizational utility. In that sense, it is essential that banks recognize that data governance is not just an IT problem but rather requires an organizationwide effort.

It can be tempting to look for a breakthrough software application or a powerful new platform that will enable banks to begin taking advantage of today’s AI and machine learning capabilities. In reality, however, such technology is not available as a single off-the-shelf installation. While platforms exist to help accelerate the process, developing the types of customer insights and analytics being addressed here involves a process, not a purchase. The applications themselves must be built and assembled from various components, and the nature and configuration of those components will vary from one organization to the next.

An accelerated path toward improved insight from data analytics

Although advanced analytic capabilities cannot be achieved with a single application, this does not mean that the implementation of AI and machine learning must be an extended or drawn-out process. Banks can begin to reap some of the benefits of this technology in a relatively short period of time, provided they approach the effort in a logical, disciplined, and structured manner.

Such an approach will begin with data discovery – that is, understanding the data that is available as well as the linkages, data quality concerns, and existing data governance processes that already are in place. With that baseline established, the initiative can move into exploratory data analysis, including descriptive statistics and the identification of data trends and initial insights.

This analysis provides the information needed for model design and development, including the application of preliminary algorithms and model testing. These models then are validated, refined, and finalized before being put into operation. Such an active and disciplined implementation effort can begin producing tangible results in a period of several months rather than several years. This is essential in view of today’s fast-changing technology – not to mention today’s rapidly evolving competitive environment.

The application of advanced analytics is taking many of today’s most successful banks into new approaches and new capabilities that affect virtually every area of their operations, including customer relationship management, business intelligence, strategic planning, marketing, sales portfolio management, risk management, and compliance. As the competition from alternative providers of financial services continues to intensify, the number of banks making use of machine learning and AI technology is virtually certain to increase – along with the demand for more advanced tools and technological capabilities.
 

Contact us

people
Mohammad Nasar
Chris Sifter
Christopher Sifter
Principal