Optimizing the Balance Sheet: Balancing Liquidity, Capital, test Q and A 11/9
What are the most effective ways to identify and prioritize the key constraints and optimization levers in balance sheet management across liquidity, capital, and interest rate risk?
To start, good reporting is critical. A collection of reports which identify key metrics to inform balance sheet health and regulatory constraints must be well understood across various functions. Whether it be capital, liquidity, funding, interest rate risk, or earnings volatility, firms must determine which constraints are worthy of balance sheet decisioning vs. reporting compliance. When these metrics are well understood, management can then prioritize the highest-value optimization actions, including asset mix, funding composition, hedging strategies, pricing, among others. Utilizing Funds Transfer Pricing (FTP), capital attribution, and liquidity charging mechanisms are helpful, but these allocation methods have limitations which must be understood. A robust understanding of cashflow modeling limitations is also a critical ingredient.
What best practices can firms adopt to manage trade-offs between liquidity, capital efficiency, and profitability, particularly in periods of market uncertainty or stress?
There is no one-size fits all formula, each firm has a unique balance sheet driven by its business footprint. The severity and uncertainty of the stress should inform the level of defense to play in each situation. When bank failures become systemic, like the 2008 and 2023 episodes, excess liquidity is obviously warranted before and well after large events. This same mindset can impair earnings during non-stress periods if firms retain too many low yielding liquid assets. ALM teams must optimize profit and retain the ability to quickly pivot the balance sheet when unexpected events occur. Best practices are to have a variety of programs and tools which can be quickly deployed if needed. This toolkit is comprised of contingent liquidity sources, various derivative types for rate risk, a multitude of fixed income investments and a diverse menu of funding sources.
How can organizations build more dynamic and responsive balance sheet optimization frameworks that adapt quickly to changing market conditions and risk profiles?
A responsive framework requires coordination across treasury, finance, risk, and business units rather than fragmented optimization within individual functions. Ideally, modeling is synchronized across balance sheet forecasting and stress scenarios in a common analytical platform. Large organizations must fight against isolated departments (e.g. the liquidity group, the capital group) making decisions to improve the metrics they are focused on versus optimizing holistic outcomes. Rather than relying on periodic planning cycles, firms should continuously update assumptions as interest rates, customer behavior, and regulatory conditions evolve.
What role should advanced analytics and real-time data play in improving balance sheet decision-making, and what distinguishes leading practices in this area?
If high-quality data is accessible and reliable, than advanced analytics play a critical role. Real-time data can be useful in some avenues, like liquidity monitoring and marginal interest rate risk positioning. However, frequent data can also jostle strategy and play a distraction, as an obsession over day-to-day changes usually doesn’t do much to inform long-term balance sheet strategy. High-quality scenario simulations paired with management decision making through a business-focused lens vs. a metric focused lens will lead to optimal decisions.
Where is AI already delivering the most value in balance sheet optimization, and what practical steps should firms take to responsibly scale its use in decision-making processes?
Reporting. Agents can enable better on demand analytics vs. canned reporting. Canned reporting is helpful, but usually does not answer every question.
Data normalization. As front-line departments have matured in isolation, data is not structured consistently. AI can help expedite standardizing data between disparate data sources.
Firms should begin with narrowly defined use cases where benefits are measurable, as scope creep on individual projects can distract and delay progress. Ultimately, AI should have a seat at the balance sheet optimization table, where it can enhance expert judgement.
Yentl Rintjema
Global Senior Internal Communications Manager, Adyen
Biography coming soon
Deniz Tudor
Head of Model COE, Bread Financial
Deniz Tudor recently joined Chatham Financial, the largest independent financial risk management advisory and technology firm, providing services in hedging and derivatives. She is the former Head of Modeling at Bread Financial, a large retail and credit card lender, parent company of Comenity and Comenity Capital Banks. At Bread Financial, she led the company's model development team covering more than 100 models and tools including Treasury, Capital, CECL, Pricing, Yield Share, Climate, Operational Loss models. She previously was the Head of Financial Risk Strategy at Bread Financial reporting to the Banks' CFO and President on first and second lines respectively. Before Bread Financial, she was a Senior Director at Moody's Analytics co-leading the Credit Analytics department as well as managing and innovating several credit products. She also worked as an Assistant Professor teaching Risk Management at MBA and undergraduate levels in San Francisco. She is an experienced leader and strategist specializing in audit, enterprise risk management, economics/scenario planning, compliance/governance, product management, analytics, and AI/ML. She has been working for several years as a thought leader focusing on trends, giving several industry presentations and webinars, and writing many white papers. She has a PhD from UCSD and a MSL (Master's in Law) degree from Fordham University. She also has a graduate level Acctg certificate from Harvard and leadership certificates from MIT and Yale.
LinkedIn profileDeniz Tudor
Director of Audit & Controls , Chatham Financial Europe
Biography coming soon
Deniz Tudor
Former Controls Director, Chatham Financial
Deniz Tudor is a risk and banking executive, thought leader, and tech-forward strategist with more than 20 years of corporate, consulting, and academic expertise. She is known throughout her career for establishing new products, departments, global teams, and companies from the ground up. She has been a trusted C-suite advisor and executive partner spanning a career from Moody’s to Bread Financial and recently Chatham Financial. She’s a transformational change agent who bridges risk and business, drives innovation, builds strong relationships with regulators, auditors, investors, rating agencies, and external consultants. She has a PhD in economics from University of California, San Diego and a master’s in law degree from Fordham University. She also has an accounting degree from Harvard and leadership certificates from MIT and Yale. In her free time, she likes reading, writing, and presenting at industry conferences, running, and spending time with her family.
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