In a pivotal role at UK based business banking platform, Ankit spearheaded the development of an advanced machine learning-driven roadmap to overhaul fraud prevention strategies. This initiative targeted the critical challenge of fraudulent bank transfers, a prevalent issue affecting numerous small to medium-sized businesses without a dedicated finance function.

Challenges:

Prior to Ankit's intervention, the bank faced substantial losses due to sophisticated fraud schemes and was under scrutiny by financial regulators, including the FCA. The existing systems were inadequate in detecting and preventing diverse and evolving fraud typologies.

Solutions Implemented:

Ankit led a team of data scientists and ML engineers, that designed and implemented a multi-faceted ML model that not only differentiated between fraudulent and non-fraudulent transactions but also evolved to identify and adapt to new fraud patterns. This model integrated:

Real-time transaction monitoring: Leveraging machine learning algorithms to analyse transaction data in real-time, reducing false positives and improving detection accuracy.

Customer behaviour analysis: Utilising behavioural analytics to understand customer patterns and flag anomalies indicative of fraud.

Regulatory compliance: Ensuring all solutions adhered to FCA regulations, thereby strengthening Tide's compliance framework.

Results:

The overhaul resulted in a robust decrease in fraudulent transactions, saving millions of pounds for clients and reinforcing the bank's regulatory standing. The success of this project not only enhanced customer trust and satisfaction but also positioned the bank as a leader in fintech security innovation.

Ankit's Role:

As the project lead, Ankit was instrumental in bridging the gap between technology and business requirements. His strategic oversight and deep technical expertise facilitated the seamless integration of AI solutions into the bank's existing frameworks, driving significant improvements in fraud prevention capabilities.