Big data analytics and risk management in banks

Following the financial crisis of 2009, financial institutions all over the world have increased their focus on customer risk management.  To achieve this, financial organisations are laying down rules for credit risk and liquidity ratio levels, including enforcing regulatory acts such Basel III that has increased the amount of customer data for analysis.

Basel III has brought new compliance requirements, which places greater emphasis on governance and risk reporting across global organisations requiring banks and insurance companies to store and analyse many years of transactional data for customers.

New models of proactive risk management are increasingly being adopted by major banks and financial institutions. Big data and analytical techniques play a salient role in strengthening risk management in areas such as card fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics. Through data analytics Banks can analyse the factors that cause borrowers to default on loans and craft new programs to circumvent those factors.

Data management challenges

Big data management and analytics has become a very crucial tool in risk management in banking sector. But with so many different types of data available managing sheer volume of data is one of the biggest challenges for banking industry. Again, the challenge makes itself apparent when trying to sort through data that is useful and data that is not.

Financial institutions now have to filter through much more data to identify fraud. Analysing traditional customer data is not enough as most customer interactions now occur through the Web, mobile apps and social media. To gain a competitive edge, financial services companies need to leverage big data to better comply with regulations, detect and prevent fraud, determine customer behaviour, increase sales, develop data-driven products and much more.

Major issue related to data analytics in developing countries concerns over the relevance of the data in, its representativeness, its reliability—as well as the overarching privacy issues of utilising personal data.

Big data analytics - Africa

Banking industry generates a huge volume of data on a day to day basis. To differentiate itself from the competition, banks are increasingly adopting big data analytics as part of their core strategy. Analytics will be the critical game changer for the African banks in future.  Presently Fintech companies and Retail banking are using big data analytics more frequently.

In Africa Kenya is an example about big data usage and analytics.  Kenya has   become a centre of business opportunities through digital currency and mobile phones usage.

South Africa’s Nedbank Market Edge product has enabled its corporate merchant clients to access data about their customers buying patterns which helped them in increasing their overall profitability.

In Kenya and Tanzania with 72 per cent of the population making use of mobile banking which generates lot of data about customer’s financial transactions. Banks are using clients’ mobile money statements to verify their financial position in order to provide them loans.

Big data analytics - global

Big Data Analytics in banking sector helps early detection cases of high risk accounts thereby helping in risk management thereby by reducing cases of frauds and defaults. It also helps in increasing efficiency in terms of dealing with fraud cases. A major US bank has reduced its loan default calculation time for a mortgage book of more than 10 million loans from 96 hours to just four.

JP Morgan Chase an American multinational investment bank and financial services company generates a vast amount of credit card information and other transactional data about its US-based customers. The Big Data analytic technology has allowed the bank to break down the consumer market into smaller segments, even into single individuals, and for reports to be generated in seconds.

Latest technology behind Big Data is Hadoop. Hadoop is a software ecosystem designed to allow the query and statistical analysis of large and semi-structured data. Hadoop’s ability and flexibility to handle increasingly complex data has unlocked new opportunities for extracting value and business insights from potentially massive amounts of organizational internal data.

Deutsche Bank uses Big Data and has been performing well by making significant investments across all areas of the Bank. Deutsche Bank currently has multiple production Hadoop platforms available through Open Source, enabling a decreased cost in terms of data processing.

In the past banks used Teradata and Netezza to build data-marts and analysed data quality using a SAS application. The process was time consuming and complex. Moreover, the data-marts couldn’t provide the data completeness required for determining overall data quality.

Recommendations

In the post-globalisation era banks have to be compliant with international banking standards to manage risk effectively. There has been increased emphasis on risk management and customer service management.  Big data analytics has become very important both for risk compliance and customer satisfaction.

Developing countries have a challenge ahead about how to sort out the useful data. MOU’s, with international data analytics companies and platforms will help in resolving such challenge.

For financial institutions mining of big data provides a huge opportunity to stand out from the competition. By using data science and machine learning to gather and analyse big data, financial institutions can reinvent their businesses.

The witter is an Economist and Consultant based Kigali

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