By Manoj Reddy, Head of BFSI-IAG Treasury Practice, Tata Consultancy Services (TCS)
Corporate treasury management has long been in the shadows of finance and risk management when the time has come to modernize and build next-generation infrastructure. Although the Basel regulations developed by the Basel Committee on Banking Supervision (BCBS), such as the LCR (liquidity coverage ratio), NSFR (net stable funding ratio) and IRRBB (interest rate risk in the banking book), have warranted some technology investments, the overall treasury ecosystem has largely remained legacy in nature. During the LIBOR (London Interbank Offered Rate) transition, the technology investment was garnered more around asset-pricing and servicing systems, even though treasury had a significant business impact. Treasury management is not only the lifeline of banking operations but, if effectively designed and managed, can propel strategic business growth and provide competitive advantages. Now is as good a time as any to ideate and design the next-generation treasury management to strategically navigate the opportunities and challenges of global banking.
Recommendations for developing next-generation treasury-management systems
- Real-time cash and liquidity monitoring for optimal fund utilization: Tactical and legacy infrastructures for cash and liquidity management, which are largely manually intensive in the aggregation of cash balances globally, have primarily meant that banks have had to over-provision nostro buffers to fund their cross-border and foreign-currency funding needs. Banks will need to design their global cash and liquidity monitoring using digital technologies that can retrieve and present these balances multi-dimensionally in real-time or near real-time for more optimal provisioning of funds, especially in cross-border payments. The industry is even exploring the adoption of DLT (distributed ledger technology) to enable real-time nostro reconciliation and balance-monitoring capabilities. SWIFT (Society for Worldwide Interbank Financial Telecommunication) has carried out a proof of concept (POC) on this utility with 34 of the world’s leading banks.
- Enhancing FTP frameworks to incorporate additional risk charges: Funds transfer pricing (FTP) is at the core of establishing a robust framework for enterprise performance management and risk-adjusted profitability management. Although it has existed for decades, the process in some banks has largely remained foundational, with it capturing the cost of funds from the source and further adding a charge (interest rate) for funding the attached risk. Even though there is regulatory guidance out there for incorporating even contingent liquidity charges into it, and the industry’s trend is toward incorporating regulatory costs as well, not all banks have been able to implement this guidance holistically. With climate change and sustainability being a core focus for banks, the timing is as good as any for them to review and enhance their FTP frameworks to not only include contingent liquidity and regulatory costs but also incorporate climate-risk charges into their FTP frameworks. An enhanced framework can enable business-line-specific fund subsidization for green credit and broader cost absorption for the enterprise’s strategic goals.
- Integrated liquidity-risk monitoring and reporting: Liquidity-risk reporting has largely been focused on individual regulations and templates for daily monitoring and reporting. The next-generation liquidity reports and dashboards should aim at bringing key metrics across regulatory disclosures—such as the liquidity coverage ratio (LCR), net stable funding ratio (NSFR), the Federal Reserve’s (the Fed’s) FR 2052 and the Basel Committee on Banking Supervision’s (BCBS’s) intraday liquidity regulations—into a single, unified view and combine them with key internal measures such as rates of change in deposits, cash balances and drawings from commitments to derive a holistic enterprise view of the cash flows, funding stability, intraday liquidity, cash management and overall liquidity profile of a bank. Due consideration should also be given to incorporating key interest-rate risk-related metrics such as NII (net interest income) sensitivity, EAR (earnings at risk) and EVE (economic value of equity) into the liquidity dashboard due to their overwhelming impacts on the cash flows and subsequently, overall liquidity positions of banks.
- Enhanced stress-testing framework for measuring resilience under severe business scenarios: Although large banks have been mandated to carry out internal liquidity stress tests by their local regulators, smaller banks would greatly benefit from embedding internal stress-testing frameworks as part of their overall treasury-management frameworks beyond just liquidity risks. Having multiple scenarios under both the idiosyncratic and systemic-risk dimensions individually and combined is recommended. It is important to align the stress-testing framework to the contingency-funding plan review and testing to ensure that alternate funding sources can be effectively called upon under stress conditions when elevated systemic risk prevails.
- Forward-looking with greater scalability, agility and control: Treasury management has evolved from a back-end facilitating or support function to a key lever for business growth and transformation. Limitations on technical scalability can especially hinder the volume, depth and quality of services a business can offer its customers. Next-generation treasury systems need to be scalable to match expanding business footprints, nationally and globally; they need to have the agility to cater to varying cross-jurisdictional business contexts with multiple currencies, instruments, products, regulations, policies and business needs. Additionally, the systems should enable the implementation of the required controls with auditability and traceability to ensure business-process risks are effectively mitigated.
- Holistic multi-level early-warning framework: The future state treasury-management solution should include a robust early-warning framework based on triggers and insights gathered from both structured and unstructured data. Banks use several idiosyncratic and systemic data measures to track early-warning signals and determine their threat levels or severity for invoking their contingency-funding plans. Additionally, combining insights and triggers mined through unstructured data levers such as negative news screenings on overall market sentiments, key liquidity and funding providers, and global lead indicators can be a very effective tool in building a holistic early-warning framework.
- Adoption of advanced analytics and machine learning (ML) for behavioral modeling: Accuracy in forecasting cash flows is pivotal to determining the exact liquidity position and managing it. Excess provisioning can lead to lower returns, and under-provisioning can mean regulatory fines or reputational losses. Hence, it is important to move towards greater accuracy in predicting deposit runoffs, loan prepayments, drawings from credit lines and so on. The right historical data, advanced analytics and machine-learning models can enable greater levels of accuracy in behavioral models, which can have a positive business impact and should be factored into planning the next-generation treasury systems.
- Seamless integration across the treasury ecosystem: One of the perennial problems of the treasury ecosystem is its federated nature, with multiple systems in place for various sub-functions and processes such as funding, asset and liability management (ALM), interest-rate risk management, liquidity management and investment management. There is good reason to have multiple systems since there is no one system out there that can cater to all these specific business needs across trading and banking books. However, it is important to ensure these processes are integrated into larger treasury ecosystems through robust workflow capabilities that link the triggers for contingency funding based on the monitored liquidity positions or convey the need for hedging interest-rate risk identified in the ALM function by the derivatives management team taking an interest rate derivative (IRD) position. The strength of a treasury ecosystem lies in how seamlessly the major components are connected to enable timely business decision-making.
Treasury management in the modern era is emerging as a key function that is expected to generate additional funding effectively and efficiently, liquidating assets in times of stress in multiple and diverse jurisdictional environments with their own currencies as well as regulatory nuances and challenges. Having the right forward-looking, scalable, resilient and digitally future-proofed treasury-ecosystem management can be a distinct strategic advantage for banks that aspire to achieve business growth and expansion nationally and, more so, internationally.