Digital Risk Analysis and Mitigation in Banking
As digitalization gets deeply embedded in banking strategy, BFSI players are increasingly offering omni-channel services like internet banking, mobile banking, and banking and payment solutions via wearables including bands, watches and NFC-enabled cards. While digitalization offers significant advantages viz. better customer experience, speed, and efficiency, BFSI firms need to investigate the digital risks like payment frauds, cybersecurity breaches, regulatory and compliance issues, and so on with high priority.
Based on Mindtree’s expertise in this space, I would like to highlight key steps the BFSI firms should adopt to reduce digital risk, improve process efficiency and at the same time ensure regulatory compliance to ward off hefty penalties levied by the regulatory authorities.
#1 Embrace Business Process Automation (BPA) for superior efficiency:
Identify complex financial/non-financial processes, which are performed daily and prepare a roadmap for process automation. Leverage the right BPM tools to define and streamline the workflow for these processes.
Let’s take an example - banks receive loan requests from different channels. BPA systems can analyze all the loan requests received and classify customers into categories (existing / new, geography, demographics, etc.). Based on the analysis, the system will automatically route the NTB customer data to credit scoring systems via API calls (external interface) and fetch the credit score and report details. The system will then automatically decide the workflow that is predefined by the business to process the credit file quickly and accurately.
This will help to reduce data manipulation by end users or sourcing agents, as data received from customers is directly fetched from respective source systems (like social security number) and matched with data received from various omni-channel sources to the banks’ credit team for processing. Implementation of automated workflows system reduces risk & fraud as well as TAT, while boosting the service quality and customer experience.
#2 Artificial Intelligence & Machine Learning (AI &ML) for fraud protection:
AI and ML technologies can create data models leveraging the humongous data volumes at banks’ disposal to aid informed decision making in areas like:
- Fraud detection in areas of customer onboarding (KYC)
- Anti-money laundering
- Credit decisioning
- International cross border payments
- Compliance reporting
- Regulatory reporting
Besides improving risk management in real-time AI and ML can also help banks provide personalized services at scale.
#3 Ensure periodic system audit:
Banks must have robust systems in place with all checks and balances to avoid fraudulent activities performed by employees. Every system should be monitored on a periodic basis and issues list maintained for action to be taken. While banks understand that these issues must be fixed on high priority to mitigate the risk of penalties from regulatory non-compliance, they face some pertinent system audit issues such as:
- Data integrity issues (KYC, AML) related to the accuracy and authenticity of data collected by FinTechs, banks, financial institutions (FIs) and regulators. These issues have a huge impact on customer identification and transaction processing with relevance to remittance and cross border payments.
- Migrating to new system / version upgradation of a legacy system with inconsistent data to newly migrated system / version upgrade system. This makes it mandatory to provide dummy data to meet the system requirements, but internally it impacts data quality and reporting due to inconsistent data.
- Failure to maintain data warehouse solution (DWH as per central bank regulatory requirements. This impacts reporting, performance, data analytics and data extraction for reporting of historical data from the existing systems.
- Failure to maintain reconciliation of General Ledgers / Suspense Accounts at regular intervals. As the complex reconciliation process involves taking into account new data sources, unstructured data, new regulatory changes and the huge volume of data, any error in the report can harm the bank’s reputation image. It can also impact financial reporting for the Financial Year period. Reconciliation teams having traditional ETL tools do much of this reconciliation work manually using spreadsheets.
Banking on proactive and real time risk mitigation
In order to successfully implement a risk mitigation strategy and stay ahead in this competitive era, banks should build a data and analytics driven risk management framework. The focus should be on strengthening security measures in systems and processes that will enhance banks’ governance and regulatory oversight, while simultaneously enabling banks to improve core business competencies and generate greater client satisfaction. The trick to make this framework a success and consistently outperform competition is to review and optimize it periodically to account for new events and technologies.