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Managing Credit Risk with Big Data and Predictive Analytics

Managing credit risk can make or break a financial institution. Global economic uncertainty and the volatile financial markets today have made minimisation of risk exposure paramount for every player in the financial sector.

Digital transformation revolves around the consumer. But going digital also means the accumulation of massive amounts of consumer customer data. This includes not only the personal information of credit seeker, but also their spending patterns, payment history, previous debts, etc. Being able to make sense of data from diverse sources is only the first step. But this isn’t the only data available. Non-traditional data, such as that from social media, retail stores and call centre interactions is also being increasingly used to identify leads, as well as to upsell and cross-sell financial services and products. Making sense of large volumes of data from disparate sources has also proven indispensable to managing credit risk.

Big Data to the Rescue The introduction of neural networks, Cramer-decision trees, rules-based model evaluation and event processing (RME-EP), along with the use of regression, have all enabled businesses to gain insights from vast datasets within a few seconds. This is possible through advancements in technology bringing us artificial intelligence and machine learning, which are instrumental in combining disparate data from diverse sources to produce robust predictive models of consumer behaviour and predict risks.

Predictive analysis combines data regarding past loans, historical financial information, and geographic, psychographic and demographic information to provide insights into future risk exposure.

Credit Risk Prediction Data quality is as important as quantity for predictive analysis. The power of big data lies in its utilisation of industry best practices. Effective application of predictive analytics, employing deep learning, helps financial institutions to:

  • Create customer profiles based on their spending habits and debt repayment patterns.

  • Devise effective implementation methodologies for credit strategies.

  • Minimise risk exposure and improve risk monitoring.

  • Improve decision-making processes.

  • Provide insights into changing customer behaviours.

  • Help in customising products and services to individual customer needs.

  • Devise cross-selling strategies to align credit offerings and customer needs.

In Conclusion Predictive analytics can help financial institutions make strategic investment decisions, detect fraud, and discover accurate revenue streams. It can assist in making unbiased decisions regarding financial policies and the introduction of new financial instruments for customers. Improved budgeting, planning, and scenario assessment is bound to reduce risk exposure and enhance risk mitigation. Predictive analysis helps to unlock stable and productive operations for financial institutions tackling risks at various levels every single day.

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