Banks looking for big data solutions to address credit risks
In the wake of the recent global recession, financial institutions had to rethink how they conduct their credit approval systems. Few areas of the world were hit as hard by the financial crisis as Europe. Countries such as Greece and Spain witnessed an economic freefall which they have yet to recover from. Bloomberg reported that the rate of loan delinquency in Spain alone rose to a record 11.23 percent late last year.
Many banks now require tighter restrictions on which applicants are granted loans and extended credit. To do this effectively, they need accurate methods of determining the investment and credit risk of a given individual or company. Big data software has presented these enterprises with the tools they require to analyze credit applicants and determine their risk level.
Big data spending on the rise
A new study conducted by the financial services association, Efma, found that more members of the European financial services community are turning to data analytics to enhance their credit risk models. According to the report, more than 40 percent of those surveyed said they plan to invest in the technology. Sixty-one percent of those bankers cited risk models for screening credit applicants as their highest priority for deploying big data solutions. Also, 38 percent said they intend to increase their existing data analytics budgets.
Driving the pursuit of big data solutions in the finance industry has been a generally negative outlook on the economic future of the region as a whole. While some countries have weathered the downturn well, others are facing down the possibility of a triple-digit recession. Forty percent of the bankers surveyed expect credit, loan and mortgage delinquency rates to increase over the next six months.
Assessing risk with big data
Sophisticated analytics tools allow bankers to gain deeper insight into their clients' behavior. By analyzing information including credit reports, spending habits and repayment rates of credit applicants, big data software can determine the likelihood that they will default on a loan or fail to consistently meet payment deadlines.
"Although consumer lending is a mature process using analytics to support risk classification, marketing, underwriting and authorizations, predictive models must constantly be calibrated to accommodate changes in consumers' behavior," said Manuel Goncalves, director of the Risk and Decision Models Unit at Millennium bcp. "These changes are driven not only by the adverse economic context but also by greater mobility and social networking. At the same time, there are new and richer sources of data that can be used to improve risk management and deliver a better customer experience."
Big data resources – particularly those built on a Hadoop architecture – can extract meaningful information regarding credit applicants' past financial activity and predict accurate forecasts on their future behavior. With these tools in hand, banks can custom build the analytics software needed to reduce their risk and financial vulnerability.