Build Regulatory Technology for Financial Institutions

Case Study - Innovation

About the Client

Clients were medium-sized banks operating in Asia & Europe with high volume AML & KYC records.

Task

Build a cost-effective Big Data solution for the Financial institution,

  1. To minimize the Anti Money Laundering (AML) risk generated through its clients.
  2. To speed up the robust risk management analytics and being able to provide real-time or on-demand reports to the regulators.
  3. To build a REGTECH system in such challenging areas as monitoring and managing changes in regulation (e.g., GDPR, FCA etc.) supporting the internal audit function, credit risk, asset risk, stress testing and producing automated regulatory reports, including structural and financial reports.

Requirements

  1. Bank must dive deeper into the risks embedded in their loans, deposits, and derivatives.
  2. Regulators are no longer willing to accept ALM and LR metrics once a month.
  3. Regulatory pressure is creating a financial data analytics crisis by adding up the cost to manage traditional infrastructures incl. complex and distributed systems in the regulatory environment.
  4. Financial institution pool data and use other tricks to push data through existing ALM systems to get weekly results at best. Currently, they often use pre-aggregation techniques to circumvent technological bottlenecks, which can hide nuances embedded in the data.

Discovery

Automation, artificial intelligence, and cognitive analytics are driving significantly improved metrics and reporting and are being used to create innovative systems that transform raw data for cognitive and analytic processing.

PAYOTEK DIGITAL’s Solution

Integrate Big Data technology with risk management using CLOUD services for a complete solution. Delivering up to 15-20 times faster results using a full record set without pooling and also addressing the scalability issue through this transformation.

List of Case Studies