ROA Optimization

Our ROA Optimization Product

CogniFrame algorithms leverage the power of Quantum Computing to derive optimized samples of potential optimal solutions for Asset Liability Management to help Financial Institutions improve their Return on Assets (ROA) on their Banking Book. For risk management, we recognize Basel III's required shift to an increased capital quality requirement and the limited use of an internal rating-based approach in modeling the risk-weighted assets. Thus our hybrid optimization solution helps banks arrive at the optimal composition of capital and assets while reducing costs, enhancing returns, and maintaining an appropriate risk level. Further, our ALM solution tackles the optimization problem by deriving the optimal match between the funds and the assets in the banking book after taking multiple risk factors into account, including the interest rate risk in the banking book (IRRBB).

Active Balance Sheet Management (Banking Book) is sub-optimally handled. This impacts Return on Assets (ROA).

For a financial institution, every 0.1% Change to ROA equals $1 million per $1 billion in Assets. The impact if small changes to ROA is therefore huge. Multi-period stochastic optimization such as asset liability management (ALM) are a NP-Hard problem. Quantum computing has demonstrated advantages over classical methods in solving such problems. Standard Classical Optimization algorithms take too long even to find good solutions.

Combining the classical risk base with quantum annealing, we leverage the power of Quantum Computing to derive optimized samples of potential optimal solutions for Asset Liability Management to help Financial Institutions improve their Return on Assets (ROA) on their Banking Book.

We use a three pronged approach that includes applying Quantum with proprietary algorithms, improving cashflow matching, optimizing risk/return for existing portfolios, and identifying future portfolio mix.