Fraud detection and prevention

The regulatory compliance of financial institutions is growing at a faster pace than before. Moreover, technology firms are reciprocating with solutions at an even higher pace. Of late, emerging technologies like artificial intelligence and machine learning have seen practical applications, which were earlier restricted to theory. Multiple specialized and boutique firms are now offering domain specific solutions and use cases against the big players who use artificial intelligence and machine learning as a platform.

Institutions facing losses from financial crimes are growing at a similar pace. Fraudsters keep innovating and beating the system. This calls for a solution where technological advancements have to be leveraged to beat the fraudster and allow early identification of their modus operandi.

The following five drivers, sub-drivers and trends are redefining fraud detection and prevention requirements of financial institutions. With evolving technology,financial institutions are rethinking their organizations strategy to control internal and external fraud – in both offline and real-time modes.

The following technology (driver) matrix indicates the market potential and trends for fraud detection systems (Rule based systems, AI/ML technology) against the Source systems (conventional source systems, emerging source systems/channels)

The following Insights (driver) matrix indicates the market potential and trends for Demanding Authority (regulatory rules/policies, organizations specific rules/policies) against the data structure (structured data, unstructured data).

The following impact (driver) matrix indicates the market potential and trends for the quantum of fraud (Frequency of fraud,severity of fraud) against the timing (post-event analysis, early warning analysis).

The following authentication (driver) matrix indicates market potential and trends for the level of authentication (transaction level application level) against the stages of authentication (single factor,multiple factor)

The following flexibility (driver) matrix indicates the market potential and trends for the user type (technical user, business user) against solution usage (Coding,GUI driven)

The next decade will experience a transformational shift in the areas of fraud detection and prevention as institutions look out for solutions which provide transformational benefits and ROI.

Benefits and ROI

Getting applied to

Early detection and prevention of high severity as a top priority
Real-time curtailing of business as usual

Financial crime detection and prevention is critical in any organization. Augmenting it with Artificial Intelligence and Machine Learning goes to show how far the industry has come in both theory and implementation.

About the Author

Sharad Kumar Ragam - Practice Manager for Financial Risk & Compliance Wipro Ltd.

He has 11+ years of implementation, consulting and solutions experience, addressing the banking customers’ needs across Fraud Risk Management, Basel & Regulatory Guidelines and Integrated Risk Management.