High scores for machine learning - KPMG...one of the primary use cases of machine learning in banks and other financial institutions.3 Indeed, machine learning algorithms help banks

Mid-market banks can improve credit risk analysis by embracing intelligent algorithms

High scores for machine learning

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Machine learning in banking is certainly not new. However, amazing advances in the speed, volume, affordability, and sheer power of data processing capabilities are opening up exciting new use cases, including improving how banks assess creditworthiness, make lending decisions, and price loan contracts.

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The KPMG name and logo are registered trademarks or trademarks of KPMG International. NDPPS 771619

Banking and big data go hand in hand. And that is exactly why machine learning fits like a glove across many areas within financial services, especially credit risk modeling.

Machine learning’s advanced algorithms are able to sift through enormous amounts of structured and unstructured data to provide insights that enable better credit risk decisions, improve data monitoring, provide alerts on potential problems, detect fraud, and enable better forecasting with predictive analytics. Such information arms banks with active intelligence for credit risk management, as well as many other areas.

Many fintechs seized this opportunity early on, wasting no time embracing machine learning to invade such traditional banking turf as lending. A TransUnion study shows fintech lenders dramatic rise in market penetration in the personal loan space. In 2017, fintechs represented 32 percent of all personal loan balances, up from just 4 percent in 2012.1

To keep pace, many large banks have followed suit, welcoming machine learning, albeit at a slightly more cautious pace than their nimble competitors. Artificial intelligence (AI) market research firm TechEmergence’s analysis of the seven leading commercial banks in the United States showed that most have invested in machine learning and some in significant sums. Their initiatives run the gamut from launching virtual chatbots to interacting with customers and employees to using machine learning algorithms to detect fraud and cyber breaches.2

Machine learning clearly already plays an integral role in finance. And as the technology continues to advance, it is poised to accumulate many more valuable uses. Now is the time for mid-sized banks to jump on the machine learning bandwagon.

At KPMG, we think credit risk modeling is an area ripe for machine learning adoption and is therefore the first area where mid-market banks should focus their technology enablement efforts. Precise credit risk modeling depends on the complex analysis of tremendous volumes of data from a variety of sources. Using more traditional modeling techniques, this is tedious, time-consuming, and often prone to error. But it is the bread and butter of machine learning algorithms.

This paper will help mid-market banks embed machine learning algorithms into the internal processes involved in assessing credit default risk, pricing loans, and making credit decisions. Read on to find out how adopting machine learning can help banks drive increased accuracy, speed, and efficiency of lending decisions while reducing credit risk—and how to get started.

1 Fact versus Fiction: FinTech Lenders, Nov. 2, 2017

2 AI in Banking – An Analysis of America’s 7 Top Banks (Techemergence, Oct. 24, 2017)

ContentsWhy machine learning, why now? 2

Machine learning in credit risk analysis 3

Determining the right methodology 4

Navigating new technology landscapes 6

About KPMG 8

About the authors 8

© 2018 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. The KPMG name and logo are registered trademarks or trademarks of KPMG International. NDPPS 771619

1High scores for machine learning

Why machine learning, why now?

Machine learning is a form of artificial intelligence, which, as its name implies, can learn. Its roots date back to the 1960s, when computer scientists developed networks of algorithms to enable computers to learn through examples without humans explicitly hand coding the rules.

At that time, there was not enough data or processing power readily available for machine learning applications to be used effectively. It would take the system far too long to arrive at its predictions, and the accuracy of its predictions was too low to be useful. However, with the explosion of big data, combined with powerful processing capabilities, it is now possible to use machine learning systems at scale to solve complex, real-world problems.

Today, machine learning can identify complex, nonlinear patterns in large data sets. With each new nugget of information, the intelligent algorithms applied within a machine learning model increase the accuracy of their predictions.

Banks cannot understand how to apply machine learning to credit risk analysis without understanding the technology itself.

Bank on these machine learning advantagesMachine learning offers key advantages for banking operations, including credit risk modeling:

More data Machine learning algorithms can mine a wide variety of data in virtually unlimited amounts, and it can seamlessly manipulate data to reduce variances and make it useful.

Better performance Due to more data and the ability to capture complex relationships among variables, machine learning tools create deeper insights into risk and more accurate forecasts.

Faster results Since machine learning can learn so much on its own, it can often include new data (and new kinds of data) without requiring a human to reprogram it. That means faster adjustments—often in real time—to changing scenarios.

Better use of manpower With current operational models, employees often spend most of their time manipulating data. When machine learning eliminates much of that tedious work, people spend their time on analysis and on building better models.



Machine learning in credit risk analysis

Machine learning algorithms can analyze the past spending behavior and patterns of a loan candidate to help a bank identify the appropriate credit allowance for that candidate, as well as pinpoint the candidate’s risk of default. True, this is just what a traditional underwriting scorecard might do. But with its powerful processing capabilities, the machine learning algorithm can process larger quantities of data and produce more accurate predictions.

What is more, the machine learning algorithm can conduct the credit risk analysis much more completely and with fewer mistakes. Earlier software could only analyze predefined and structured data, such as a loan applicant’s transaction and payment history, that is put into a standard format. With machine learning, financial institutions do not have to work so much to prepare data for analysis. They can “dump” large amounts of varied data into the system and just let the algorithms analyze it.

As such, the algorithm’s findings and insights are more precise because they are grounded on a much deeper set of data—not only transaction and payment histories standardized and stored within the bank’s systems, but also nontraditional and usually unstructured data sources, such as social media posts, phone calls, and text messages. This kind of “alternative data” can provide a more nuanced view of an individual’s credit risk and improve the rating accuracy of loans.

Machine learning capabilities become even more valuable to credit risk modeling for new customers or those without a credit history. With machine learning, banks can use new sources of data to assess potential borrowers who previously did not have enough historical credit information from which to generate a credit score, thereby potentially increasing credit access to new customers.

A report by the Financial Stability Board includes assessing credit quality as one of the primary use cases of machine learning in banks and other financial institutions.3 Indeed, machine learning algorithms help banks process the information needed to make sophisticated credit risk decisions much more efficiently and effectively—often with minimal human intervention—all while limiting incremental risk.

3 Artificial intelligence and machine learning in financial services: Market developments and financial stability implications (Financial Stability Board, Nov. 1, 2017)

Underwriting Machine learning tools can look at millions of data points from consumer histories and from the broader economy. That means better forecasting and, therefore, more accurate risk-based pricing.

Fraud detection Machine learning algorithms can quickly compare transactions against multiple data points to look for fraud. Unlike older, rule-based models, machine learning tools usually do not need an update to catch new patterns. Its algorithms figure them out on their own.

Loss forecasting Machine learning’s ability to integrate a wider variety of data and identify subtle relationships in the data can produce more accurate and granular forecasts.

Customer relations Machine learning can mine data to match new products to existing loan customers, and it can prioritize which defaulting payers to try to collect from first. In addition, many banks are exploring the use of chat bots to enhance the customer experience for borrowers.

Numerous areas of credit risk analysis can also benefit from machine learning:



Determining the right methodology

Unsupervised machine learning algorithms take in unlabeled data and look for commonalities in the underlying characteristics of the data. They detect hidden patterns to inform their decisions or actions. When Amazon recommends a book to a customer, it is using unsupervised learning to share the book purchases of similar groups of people to that customer. An example of unsupervised learning in the finance industry is an algorithm that prices new securities by comparing the security to existing securities with similar characteristics.

Supervised machine learning algorithms apply what has been learned in the past to predict the outcome of new data. They are trained with labeled data, which teaches the algorithm to distinguish and classify data points according to explicit “rules.” As it sees more and more data, the algorithm “learns” to accurately classify new data to inform various decisions. For example, an algorithm that is fed training data that contains labels for fraudulent versus non-fraudulent transactions will ultimately learn to detect fraud in new transactions.

Since history is the best variable at predicting the probability that a loan applicant will default within the next 12 months, supervised machine learning algorithms are most common in credit risk analysis applications. Algorithms that are fed training data that labels different characteristics of loan applicants who did default on a loan versus those who did not (i.e., occupation, salary range, credit history, etc.) can learn to predict the probability of loan default in future borrowers based on those characteristics.

Three machine learning models for credit risk analysisThe right methodology for your organization will heavily depend on the data you have and how you apply it.

Support vector machines use geometry to classify data points to achieve more accurate results. Essentially, these algorithms draw a boundary line in the data that best separates the levels of the model outcome to identify the optimal set of predictors. They are especially useful when sample sizes are small and the number of variables is high.

Ensemble models create multiple models and then combine them to produce a much more accurate result than the individual models, can produce on their own. By combining multiple models, there is a higher likelihood than the combined results are more accurate and reliable than what any single model could produce.

— Random forest is an ensemble model that uses simple decision trees to split data and find correlations with certain target variable outcomes. This method leads to significant improvement in prediction accuracy.

— Gradient boosting machine is another ensemble technique that also typically uses decision trees. The model is built iteratively to provide increasingly accurate predictions.

Machine learning involves two types of learning: supervised and unsupervised learning.





Navigating new technology landscapes

Of course, banks will still need to overcome some other challenges relating to reporting, culture, skills, and infrastructure to seize full advantage of machine learning.

— Complexity: These algorithms can be opaque and hard to understand, which represents a challenge for highly visible estimates. For example, one of the most pressing challenges for lending institutions is the new Current Expected Credit Loss (CECL) standard, which will require banks to change their approach to estimating and accounting for credit losses.4 While this is a great opportunity to apply machine learning techniques, regulators and other stakeholders tend to prefer models that are relatively simple and transparent.

— Cultural issues: As availability of data has increased, banks’ techniques for modeling across the lending cycle has not kept pace with changing times. With machine learning, many of the challenges associated with the traditional underwriting methods can be overcome. But there is always the fear of change that makes people resist it.

— Skills shortage: To implement machine learning, banks will need to hire and train people to find the right data and to build, maintain, and monitor models. Most financial institutions do not have enough experienced machine learning talent on staff. The right people need more than a computer science background. They also need to know their way around statistics, regulatory compliance, controls, and cyber security.

— Infrastructure issues: Making data science tools like machine learning algorithms work with legacy platforms and databases sitting in silos is no easy feat. All the typical reasons that slow down technology transformation apply, including sunken costs in legacy technology and models and organizational structures designed to do things the old-fashioned way. Many organizations already have enough trouble with their existing processes and question where to even begin making the change to machine learning.

In the past, data was expensive to collect, store, and maintain, but the revolution in big data has enabled dramatic drops in the data costs. With relatively cheap data, the biggest obstacle to adopting machine learning has essentially disappeared. The algorithms themselves are generally open source, so there is no cost there. Machine learning for credit risk analysis can thus provide an outsized return at a relatively low cost.

4 Ten key credit risk & lending challenges facing the financial services industry in 2017 (KPMG Americas’ FS Regulatory Center of Excellence, 2017)



Three steps to get startedWhile larger banks have started using machine learning, mid-sized institutions should seize the opportunity to do so. They can apply machine learning to improve every stage of the credit life cycle from underwriting to loan forecasts and managing customer relationships. With its ability to consume and interpret data, machine learning can be useful for making better decisions about nearly every kind of loan.

So how do banks get started? We have identified an initial three-step road map to help get machine learning implementations up and running in credit risk processes.

The faster banks embrace machine learning, the faster they will see the benefits of improved credit risk analytics on their bottom line. The journey can seem overwhelming, but the most important thing is to start exploring. To remain relevant, stay competitive, and keep their customer data secure, banks will need to explore and embrace transformational technologies like machine learning. And the sooner, the better.

Assess your current state. Some banks have been using the same credit risk models for years. What has changed since they first deployed the model? What untapped data do they currently have (or could they get) to attain better insights? Where in the organization does it exist?

Make the business case. The many benefits gained from the machine learning investment will outweigh the initial expense. Banks should justify their investment by determining the resources they will need to provide a seamless blend of speed, convenience, and security, enabling scalability that spurs growth and customer satisfaction while leveling the playing field against fintechs and larger-sized competitors.

Look for talent. The right talent is a critical success factor in implementing machine learning in the lending cycle. Who will banks need to reassign, retrain, or hire to build and maintain the right models? Where can they find the skill sets needed? How will they build a new culture to support and encourage future talent?

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About the authorsJeffrey E. Bower is a managing director in KPMG Advisory’s Risk Analytics practice specializing in risk analytics. With more than 30 years of experience, he has performed extensive portfolio credit risk and loan performance analyses; designed, implemented, and upgraded complex credit risk forecasting systems; and reviewed and improved credit operations and credit scoring systems. His management responsibilities have covered consumer lending operations, credit risk management, corporate and strategic planning, financial analysis and corporate accounting, and MIS operations.

Alberto Sanabria, CFA, FRM is a director in KPMG Advisory’s Risk Analytics practice, specializing in risk analytics. He has more than 14 years of experience advising financial services institutions on credit risk management processes, model risk management, and model development for consumer credit portfolios. He also has deep knowledge of underwriting and behavioral scorecards; stress testing (CCAR, DFAST); credit loss, prepayment and valuation models; Allowance for Loan and Lease Losses (ALLL) practices; accounting and regulatory guidance; and modeling methodologies.

About KPMGFrom financial leaders to regulators to consumers, concerns about credit risk are top of mind for everyone who touches the global financial system. KPMG’s Credit Risk team has extensive experience helping financial institutions identify, assess, manage, report, and limit credit risk.

We work with some of the world’s leading firms to improve processes, governance, and strategy related to credit risk management. Through our services, clients are able to effectively measure, predict, and control credit risk, and apply analytical approaches to complex operational business challenges in consumer lending.

Our services are underpinned by our deep knowledge of new digital technologies, which are driving new levels of process automation and credit data and analytics to enhance all aspects of credit risk management. By applying advanced technology to lending and credit operations, we help clients seize vast operational opportunities across the full credit life cycle: acquisitions, underwriting, customer and portfolio management, and delinquency and collections strategies.





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Contact us

Jeffrey E. BowerManaging Director, Risk AnalyticsKPMG AdvisoryT: 610-989-3668 E: [email protected]

Alberto SanabriaDirector, Risk AnalyticsKPMG AdvisoryT: 202-533-4428 E: [email protected]

The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation.

Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates.


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High scores for machine learning - KPMG...one of the primary use cases of machine learning in banks and other financial institutions.3 Indeed, machine learning algorithms help banks