How Machine Learning can help Risk Managers in Customer Risk Profiling?

machine learning in customer risk profiling

As digital lending continues to grow, firms are looking for ways to make their services more efficient and profitable to both lenders and borrowers. The consumer lending business is centred on the notion of managing the risk of borrower default. Historically, lenders used to make go-no go loan decisions based on a loan applicant’s credit score. But this approach does not paint a complete picture of a loan applicants’ creditworthiness.

Now with technological advancement, firms are building more complex techniques to identify risks. Here, businesses can effectively use Machine Learning (ML) to identify risks, which is based on the premise that machines can learn and adapt from experience rather than rely exclusively on pre-programmed logic.

Using Machine Learning techniques, businesses can build Customer Risk Profiling models which enable the identification of likelihood and probability of customers being a risk.

Machine Learning revolution in lending

Machine Learning, an extension of AI has come to play an integral role in many phases of the lending arena, from approving loans, to managing assets, to assessing risks. Machine learning can process terabytes of data in seconds, which humans could not process in a lifetime. This sheer power of modern computing will lead to faster loan origination, fewer compliance problems, and more inclusive lending overall.

According to one of the McKinsey Quarterly report, firms who have replaced older statistical-modelling approaches with Artificial Intelligence techniques have experienced,

  • 10% increase in sales of new products
  • 20% saving in capital expenditure
  • 20% increase in cash collection
  • 20% decline in churn

ML applications will speed up online lending. Online brokers, lenders and credit bureaus can use algorithms to assess eligibility for credit. On the flip side, ML can also match business owners with the right lender. ML analyses and authenticates users’ transactional data, and income verification and spend analysis, helps highlight risk factors used for a richer credit scoring experience. This will reduce the risk of default and increase borrowers’ financial profiling. It can also help automate parts—and maybe all—of the process.

How ML can be used for customer risk profiling?

In traditional risk modelling, customer segmentation is based on “hard” lines and broad categories, such as new customer vs. existing customer. This doesn’t capture the behaviour of certain individual entities or more optimal ways to segment scoring models. But using ML algorithm, firms can segment customer profiles based on behaviour.  Composite risk profile can then be quantified to a risk score that can be used for a variety of decision-making purposes.

*ML algorithms can recognize any specific pattern from a large chunk of data

*Insightful decisioning can be derived through ML. 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

*Various Data Visualization techniques and reporting to help identify trends in customer behaviour, product performance, store performance and identify customer delinquency patterns.

*Identify high loan balances of a certain customer, to identify fraud.

Thus, using Machine Learning techniques, lending firms can effectively build Customer Risk Profiling models which enable the identification of likelihood and probability of customers being a risk.

How Insight Consultants can help you?

Insight Consultants specialises in digitizing the lending ecosystem. From general to specialised, prime to sub-prime, we have helped organization evaluate data structures and availability, free up information from siloed systems, and identify the richest areas for machine-fuelled insight and improvement. It is time for Risk Managers and financial services to embrace the benefits of Machine Learning. We can help you provide actionable advice to begin ML initiative with the right approach and perspective. So, if you are you looking for ways to harness the power of machine learning to analyse the credit worthiness, or would just like to know more,

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