Customer Risk Profiling using Machine Learning in Lending
With technological advancements and Big Data, businesses are building more complex techniques and algorithms to identify risks. Using Machine Learning techniques, businesses are able to build Customer Risk Profiling models which enable the identification of likelihood and probability of customers being a risk.
How Machine Learning can Re-define Lending?
Machine learning, is beginning to create new avenues in the lending market. Machine Learning is an extension of artificial intelligence, that enables computers or robots with the ability to learn, analyze and predict, using algorithms that iteratively learn from data. It empowers the system to learn and adapt itself. Today, it is being implemented in industries ranging from financial services, healthcare and retail to transportation, and multiple domains like accounting, audit, marketing and sales.
According to one of the McKinsey Quarterly report, in Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine learning techniques. By doing this, some of them have experienced a 10% increase in sales of new products, 20% saving in capital expenditures, 20% increase in cash collections, and 20% decline in churn.
Advantages of using Machine Learning in Lending
Machine learning algorithms have gained popularity owing to a wide range of advantages:
- Pattern Recognition: Machine learning algorithms are useful to recognize any specific patterns from a large chunk of data.
- Insightful Decisioning: It could filter irrelevant data, capture relevant information and process just them to offer an in-depth insight from the available data. This prevents users from falling prey to wrong judgements, offer them a complete picture of the current scenario and help them arrive at better-informed, insightful decisions which have a high probability of success.
- Self-Modifying: The new age machines can modify and update their algorithm through self-learning principles which ensure that the self-rewrites do not turn out to be a catastrophe with a user-defined function that classifies the modification, weights its fitness and buckets them as an error or reward.
Customer risk profiling using Machine Learning
When a short-term unsecured loan business wanted to expand its loan book and increase bottom line, they launched a new product like Installment loan. An installment loan is a loan for a specific amount of money that is repaid with interest through a series of fixed monthly payments. The interest rate may depend on the financial history of the applicant and loan size and repayment terms can range from a few months to over 30 years.
For an impactful bottom line performance, the short term unsecured loan business needs to predict defaulters and non-defaulters, predict insolvency in time and at the same time lower the NPA levels. The major challenges companies face in this business are aggregating disparate data sets to get a comprehensive view of customers, monitoring and predicting potential impacts & establishing continuous feedback among KYC, sanctions, transaction monitoring and analyst data. The approach of Customer Risk Profiling can be adopted in this case and should include:
- Machine Learning Algorithms to help identify key variables from 500+ variables.
- Feature engineering mechanism and algorithms to help select important variables based on weightage.
- Various Data Visualization techniques and reporting to help identify trends in customer behavior, product performance, store performance and identify customer delinquency patterns.
- Identify high loan balances of a certain customer, to identify fraud.
- Machine Learning algorithms facilitate customer segmentation based on certain variables and come up with strategy for each customer segment.
- Reverse engineering of competitor data attained from Credit Bureaus.
This lead to saved cost on additional data information from Credit Bureaus, 47% increase in Loan book in a quarter, improved approval rate & identification of best and worst performing loan product.
Developing unique machine learning algorithms has the potential to address all complex problems faced by lenders and achieve their goal through disruptive yet sustainable innovative techniques. It seems to be apparent that more and more lending companies will enter the domain of machine learning and Big Data which will make lending even more quick, accurate and easy. It is merely a matter of time before the technology companies and fintechs will integrate an organic layer of machine learning in their already dynamic platforms.
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