As developed countries experience a slow but steady recovery, credit risk managers in emerging markets face growing default rates as household debt continues to rise with little relief in sight. The Institute of International Finance stated at the end of 2015 that global household debt had risen by $7.7 trillion since 2007 to more than $44 trillion, and that $6.2 trillion of that rise was in emerging markets. Household debt per adult in emerging economies also rose by 120 percent over that period to some $3000, it added.
In order to survive and thrive in this economic climate, credit risk professionals need to consider innovative means of decreasing default rates and improving the accuracy with which credit is issued. One such way is applying data analytics to Big Data. (Click to Tweet!)
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The value of your data is limited only by its volume
Big data refers to massive datasets that can be captured and subjected to an analytics platform to detect patterns, trends and preferences that inform businesses about their customers in a variety of ways. In fact, the insights you can derive are only limited by the kind of data at your disposal. Banks around the world are beginning to understand the potential of their data in credit risk management practices, and are increasingly adopting data-centric cultures within the business.
According to a report by the Economist Intelligence Unit, the vast majority of banks in all sectors either currently support the use of big data analytics as a tool in credit risk management, or plan to do so soon. The data can be applied to inform and improve credit risk management as well as liquidity risk management. Credit and liquidity are listed by bankers as the two greatest risks their institutions will face over the next three years. Credit risk managers should see this as an opportunity, which can be best in the following ways:
- Minimise Fraud and Non-repayment: 45% of bankers surveyed by the Economist Intelligence Unit in 2014 found data analytics to be useful in preventing both fraud and non-repayment. When a customer applies for a loan, the data captured at the time of application eventually becomes outdated. Until recently, it was difficult to determine whether and how the person’s circumstances had changed. Now, data from the customer’s payment behaviours, interactions with the financial services provider (for example contact via websites and call centres), and even their social media activity can be drawn on to confirm their legitimacy and understand their financial positions better.Click here to read how Social Media and Mobile data are changing the face of credit risk.
- Broaden the Market: By analysing mobile and social media data, data insights can also make it possible, or easier, for people without much in the way of credit history, to get credit, while minimising the risk to the provider. This substantially broadens the market, creating new customers and revenue streams.
- Marketing to Low-Risk Customers: Another key use of analytics involves marketing. Customer habits and patterns can be identified, providing insights into behavioural triggers and patterns. Financial service providers can get to know their customers and their needs better, and tailor products accordingly. For example, seasonal spending patterns can be identified and used to adjust credit offers targeted to the times of year when their customers need them the most, but are able to pay them off within the agreed-upon timeframes.Click here to read more on how Data Analytics is turning Credit Risk Managers into Information Addicts.
The value of big data insights to the retail banking industry alone is estimated at billions of Rands. It has the potential to transform the financial services sector the same way that computers did in the 1980s and 90s. Leveraging data insights in the realm of credit risk gives businesses the agility to react to opportunities and better protect themselves against risk that is so prevalent in the industry. Contact Principa to find out more about how data analytics can benefit you.
