"...rather than a platform becoming a focal point for the best ideas from the best people, each institution must try to solve the many problems effective risk management presents as best it can."

Until now.

Every financial institution’s health depends on its ability to manage risk to high precision. Indeed, failure to control risk has relegated many once-venerable companies to the history books. But managing risk is a complex mixture of both science and art. Data, quantitative analysis, and business expertise must all come together efficiently and accurately. Decision makers need information about the risks they are responsible for in a form they can understand, communicate, and most critically, act on in a timely manner. In the absence of this, management blindspots not only hide potential opportunities, but also allow risks to go unchecked that may ultimately overrun and destroy the company.

Implementing a commercial credit risk management framework is unlike fulfilling most other system needs in banking because, despite its critical importance (along with the umbrella of regulation that covers much of this area) there are no effective off-the-shelf solutions available. Big banks in their commercial lending divisions typically have built their own, while smaller banks get by using a variety of ad hoc measures. Instead of benefitting from the economies of scale that a standard platform for this critical infrastructure would deliver, every financial institution must shoulder the entire cost alone.

Until now.

We introduce the Risk Framework Manager (RFM)– a risk management architecture embodied in software tools and associated practices. Unlike most existing approaches, which concentrate on delivering operational capabilities to financial institutions and “bolt on” limited forms of risk management capability as an afterthought, RFM is designed from the outset as a unifying framework that integrates with key functional areas throughout the organization and creates a more complete, more unified picture of risk more efficiently and cost-effectively than any existing solution.

The software collects and analyzes risk-related data from many sources both inside and outside the institution, and uses this to provide near-real-time visibility into risk at all levels of the organization, from front-line banking, to senior management, in a way that has never been possible before. For the first time, managers can have up-to-date, accurate risk-related information and “early warnings” at their fingertips, and run powerful simulations in an intuitive, “drag-and-drop” interface. On the “shop floor”, RFM’s unique model-driven metadata architecture slashes go-to-market lead-times due to risk model updates by automatically propagating these to the business systems that use them, without the months (or years) of IT development that is typical of today’s practices. RFM’s modular design, along with its ease of integration, allows it to be deployed on a module-by-module basis, filling the highest priority gaps in an organization’s risk infrastructure while integrating with what exists, removing the need for wholesale replacement of existing tools just to take advantage of a few new, key features. The modules include Wholesale Loan Origination, Model Configuration Management, Probability of Default/Loss Given Default/Exposure at Default models and data management, Regulatory Documentation, Governance and Oversight, and the Stress Testing Cockpit. Our thought leadership in real-world risk management, data sciences and software design has produced a number of breakthroughs in “bank-tech”, including the following:

• By storing metadata in the risk-modeling system, front-line applications automatically re-configure themselves when a change is made to a risk model (e.g. a PD model for a commercial lending segment). This eliminates months, and sometimes years, of development lead time from the analysis phase to deployment of new model features since extensive IT software re-engineering is no longer required.
  RFM's unique Model Injection Architecture saves man-years of IT effort every time a model change is required.
• All risk models are versioned, centralized, and linked in real-time to available data. This makes it possible to provide senior management with a near-real time view of the institution’s current position via RFM’s “Risk Cockpit” dashboards..
• Where data is sparse or traditionally unavailable, advanced machine learning techniques can be used to locate key information in a way previously impossible. For example, web presence and social media often contain information from which early-warning indicators can be obtained relating to companies to whom a lender has exposure, but about which little public information has traditionally been available.
• A new, visually interactive “what-if” approach to Stress Testing gives managers the ability to easily create and run stress scenarios and evaluate their impact on the portfolios for which they are responsible. The ease of use hides the sophistication of the underlying modeling paradigm, making the tool both user-friendly and very powerful.