Insights Driven Business and Operations Framework

Challenge

The impact of the COVID-19 pandemic on capital market firms has been significant.

Capital market firms have especially faced a major jolt due to market uncertainty, near-zero interest rates, shrinking liquidity and funding, credit tightening, and a spike in operational tasks. This has created immense disruption in the short to medium term, leading to decreased consumer confidence, lower margins, increased costs, and reputational risks. To thrive in the post-pandemic world, banks and financial services firms require a data-driven approach to create more value for the business and customers.

Solution

TCS’ Insights Driven Business and Operations Framework enables banks to harness banking data using analytics and machine learning.

The framework improves operational efficiency and business value and helps banks overcome capital market challenges, align their businesses to the post-COVID-19 world, and explore new revenue opportunities. 

The key features of the framework include:

• Predictive analytics using TCS’ Digital Twin model to create a digital replica of capital markets processes for resilience, risk reduction, and improved efficiencies

• Predicting future cash and liquidity needs and shortfalls by modeling enterprise resource planning (ERP), treasury, general ledger (GL), and business operations using cash history, capital budget, operating budget, business units, credit facilities, and investments data to adapt to changing market conditions

• Data monetization framework to identify and derive revenue-generating data sets and related use cases across domains

• Front-to-back insights to drive business outcomes and adopt a purpose-driven approach

Benefits

TCS’ framework drives seamless implementation of artificial intelligence (AI) and machine learning (ML).

The framework enables financial services firms to:

• Predict failures to minimize exceptions and conduct root cause analysis of issues to achieve efficient resolution through front-to-back insights using machine learning techniques.
  
• Eliminate waste and optimize work packets through risk-based profiling of transaction data.
  
• Drive incremental business value by delivering purpose-driven client services.
  
• Conduct behavior modeling at each process step using internal and external structured and unstructured data sets to predict all possible outcomes, resulting in business value, risk reduction, and higher efficiencies.
  
• Find new sources of revenue and create business value through monetization of internal and external data.