Using Analytics To Prevent Banking Fraud

by Sohini Bagchi    Oct 14, 2014

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As customer retention and loyalty have become the key focus for banks globally as well as in India, they are increasingly using data analytics tools and technologies for combatting risks and frauds in order to protect customer data. In an exclusive interaction with CXOtoday, Mohan Jayaraman, MD, Experian Credit Information Company of India and Country Manager, Experian India explains the importance of data analytics in fraud detection and management.

How banks and financial institutions can improve customer lifecycle using data analytics? 

The effective use of data analytics can aid in enhanced customer lifecycle management in terms of heightened customer intelligence, new sourcing mechanisms, better customer monitoring techniques, optimal cross sell and of course, a smooth collection process. Data analytics can be used to understand consumer behavior, for segmentation and to develop the right offer for the right customer. The golden rule for most banks is to nurture customer relationship and enhance the customer lifetime value.

Banks can also use data and analytics to ensure responsible lending. While credit bureau data can play a crucial loan in customer management practices, Decision Analytics help banks and NBFCs undertake effective risk management measures and provide better customer service. Banks can also use data and analytics to reduce delinquency, manage cost of collections, manage attrition and reduce wasted time. It is all about knowing the right value of the customers to understand optimal customer treatment.

How is it possible to curb fraud at the application level? Can you tell us how it can work for banks and financial institutions?

It is possible to curb fraud at the application level by using the right solution. In fact, a continuous threat that financial service providers grapple with is fraudulent applications. The most effective strategy is to prevent the fraud at the point of application. This requires the authentication of genuine customers and the detection of potentially fraudulent applications before the customer is underwritten without adversely affecting customer service level and speed of decision making. For example, Experian’s Hunter is a fraud detection inconsistency service that has the ability to tackle industry wide frauds. It operates at two levels: Local hunter enables banks to check internal inconsistencies and past records within the bank across branches. The national hunter service facilitates in tracking inconsistencies not just within the bank but across banks which have signed up to be members of the closed User Group.

Does Experian have a common platform for sharing negative non competitive data of fraudsters. Can you tell us how it works?

At the loan application stage, most banks find it difficult to access data with respect to an applicant’s past loan rejections. Increasingly, banks are on the lookout for information that would summarize an applicant’s past loan rejections at an internal as well as an industry level. Fraudsters will exploit blind spots by moving between channels and geographies. Experian’s National Hunter lays more emphasis on layered defenses which help prevention of fraud at the point of application. This saves banks from accepting fraudulent customers. Use of analytics in fraud management, has revealed that out of the various fraud types, fraudulent contact information contributes to 18%; use of fictitious identity:15% and repeated attempt from already identified individuals 19%. There are more inferences which a fraud and management system like Hunter can derive from sharing non-competitive data of fraudsters across organisations and businesses.

What is the potential of big data in the banking sector?

Most Indian organizations are still grappling with the amount of data they generate. The early adopters of analytics are expected to emerge from sectors such as BFSI, retail, hospitality and media. The challenge faced by most sectors is to analyze the data collected and to identify new opportunities and store them securely and affordably. Banks can especially leverage analytics to innovate and transform internally as well as through products and experiences. Moving towards data-driven and evidence-based business models allows an enterprise to understand its customer and empower its workforce. In the financial services sector, the benefits of the analytics initiatives are likely to translate into a better customer experience and operational efficiency. Economic Customer acquisition and customer management are the big challenges and analytics initiatives will definitely help in managing these problems in a data driven manner. Leveraging data sources like the credit bureaus in conjunction with internal data initiatives will help structure data better to make it actionable.

What is the future of data analytics in fraud management?

The use of analytics in fraud management enables businesses to conduct safe business with their good customers while denying ever-persistent fraudsters. Today, there are application fraud management services such as Hunter available to tackle industry wide frauds. Evolving rule-based fraud detection techniques are critical as fraudsters are fairly capable of finding innovative ways of breaking through challenging defenses. With businesses being more vulnerable than ever before, the significance of fraud management has never been more pronounced and decision analytics is a step in the right direction.