Credit risk modeling and analytics
Advanced credit risk analytics enable institutions to improve underwriting decisions and increase revenues while reducing risk costs. We work across all asset classes, credit risk models, and the entire credit life cycle, including profit maximization, portfolio management, and loss mitigation. We cover a wide variety of sectors, including banking, telecommunications, and government agencies. Our work helps clients address six strategic imperatives:
- Understanding and adapting to changing consumer behavior
- Mining the vast amounts of available data
- Expanding the credit “buy box” without altering the overall risk profile or appetite
- Increasing penetration of the customer base
- Containing credit risk within the portfolio
- Understanding aggregate risk levels in a range of baseline and stress scenarios.
Stress testing and balance-sheet analytics
A structured, well-defined stress-testing process connects the “engine room” to the board room. It goes beyond cumbersome exercises aimed solely at achieving regulatory compliance and moves board members and business leaders to action. Our stress-testing capabilities include generating scenarios, translating them into environmental parameters through macroeconomic quantification, and then assessing the impact of these scenarios on the market, and on the client’s profit and loss (P&L) and balance sheet. All of this informs an action plan to mitigate risks and swiftly capture opportunities.
Operational risk and fraud analytics
We provide advanced operational risk and compliance analytics, protecting clients’ P&L and capital. We support both financial and nonfinancial institutions in solving the most complex analytical problems across all nonfinancial risk types. These risk types include loss and scenario-based models—for setting capital requirements (for example, the advanced management approach and the Solvency II Directive) and for stress testing (for instance, the Comprehensive Capital Analysis and Review in the United States, the Prudential Regulation Authority in the United Kingdom, and the European Banking Authority and the European Central Bank in Europe). We also cover advanced models for operational risk and compliance management, such as anti-money laundering, know-your-customer fraud, and unauthorized behaviors.
We support clients on a broad spectrum of model risk-management topics: defining model governance, policies, and procedures; identifying model needs; validating models; and providing the necessary organizational support, capabilities, and culture. Our deep capabilities in advanced analytics allow us to assist clients in model validation across all model classes. Our recent acquisition of Risk Dynamics, a leading risk-analytics firm, further bolsters our abilities in this burgeoning area. Together, we help clients transform and enhance their risk-modeling capabilities and strategies, and we establish robust and sustainable business models.
We help clients complement traditional risk analytics with machine learning to find previously unidentified patterns and make better predictions. For example, we use machine learning to improve fraud detection and mitigation, improve underwriting decisions, and optimize collection efforts. Our efforts with a Latin American telecommunications provider enhanced its collections process and identified the 15 percent of customers causing 83 percent of losses.
We help institutional investors—particularly large public pension plans, sovereign-wealth funds, and endowments—better understand the risk and return potential of their portfolios. We help clients analyze risk factors, model asset and liability matching, and enhance their asset-allocation methodologies. We also help them disaggregate the sources of investment performance and costs to get a fuller view of their risk-adjusted returns. Increasingly, we help institutional investors use insights from big data and machine learning to stay at the cutting edge of their disciplines. We embed these insights and techniques into ongoing investment and risk-management processes to help clients build lasting capabilities.
We help insurers apply big data and advanced analytics to address key issues, such as underwriting and pricing. In doing so, we help optimize new-customer acquisition and maximize economic value from policies to ensure that clients settle claims fairly for customers and shareholders.