While the developed nations are slowly recovering from the financial chaos of post depression, the credit risk managers are facing growing default rates as household debts are increasing with almost no relief in sight. As per the reports of the International Finance which stated at the end of 2015 that household debts have risen to by USD 7.7 trillion since the year 2007. It now stands at the heart stopping amount of a massive USD 44 trillion and the amount of debts increased in the emerging markets is of USD 6.2 trillion. The household loans of emerging economies calculating as per adult rose by 120 percent over the period and are now summed up to USD 3000.
To thrive in this market of increasing debts, credit risk managers must consider innovative methods to keep accuracy in check and decrease default rates. A good solution to this can be applying the data analytics to Big Data.
The value of data lies hidden in its volume and is only limited by it:
The term Big Data has been defined and redefined by many, it is basically a data set that can be captured and subjected to any platform of data analysis to identify patterns and trends that can generate useful insight to amplify business results. These data analytics processes give businesses a variety of insights on their customers based on different factors. Insights that can be obtained from these processes are boundless and can only be limited by the type of data at ones disposal. Worldwide banks and major financial institutions are beginning to realize the importance of their data and are employing data analytics procedures to get the most out of all information at their disposal, especially in the context of credit risk management. Data-centric cultures are slowly being incorporated in the main stream business practices around the world for smoother functioning.
Over the span of the upcoming three years the two biggest risks faced by the banks will be – 1. Credit and 2. Liquidity. But credit risk managers can use this opportunity to their advantage and that can be done in the following ways best:
- Fraud and non-repayment can be best reduced with Big Data Analytics: according to 45 percent of the bankers surveyed by the Economist Intelligence unit on 2014, data analytics is the most effective measure to reduce both cases of fraud as well as non-repayment. When a person applies for a loan the data captured during this initial process is more often than not outdated with time. Now with data analytics we can determine how a person’s behavior and circumstances changed. This is also helped by their social media activity which can further help to affirm how their financial positions changed with time.
- To broaden their market share: with proper analysis of mobile and social media data the credit risk managers may be able to gather insights and broaden their market to people without much idea of credit history to acquire credit without risking the provider. Thus, this will substantially amplify the market base with greater revenue streams from more customers.
- Reaching out to low risk customers: marketing is another avenue of business that gets huge benefits from data analytics is marketing. Analysis offers useful insights of customer behavior, spending patterns and behavioral triggers. The best way to know the needs of customers by the financial advisors and then tailor their services accordingly lies within the noise surrounding Big Data.
The value of insights obtained from Big Data analytics to the retail banking sector alone is estimated to be billions of Rands.
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