Introduction
Model Risk Management as a new risk discipline started in 2012 with the publication by the OCC of the snappy entitled “SR 11-7” that outlines guidance on how banks should manage model risk. A couple of quotes sets the scene: –
• Model Risk can lead to financial loss, poor business and strategic decision making, or damage to a bank’s reputation
• Model Risk should be managed like other types of risk
• Banks should identify the sources of risk and assess the magnitude
• A guiding principle for managing model risk is ‘effective challenge’ of models
Although technically guidelines, SR 11-7 is now referenced as a holy script by all quants. It is generic in the how it articulates the requirements for good model risk governance and is open to interpretation on what activities in a bank should constitute a “model”.
The scope of models now captured by the SR 11-7 definition has, on the request of regulators, risen dramatically. Derivative pricing and IRB/PD/LGD/EAD credit models were the first in scope and now include; capital/risk, stress testing, econometric, balance sheet forecasting, algorithmic trading, optimisers, strategic planning, independent pricing, liquidity management, pension liabilities, conduct monitoring, fraud detection, KYC and even HR screening models. The introduction of big data analytics that includes artificial intelligence and machine-learning with their “black box” characteristics adds to the overall complexity.
Response from Large Banks
Large banks have been caught out by the speed of this proliferation and need to manage the risk from their spaghetti of model dependencies. Their response has been to dramatically improve their model development and documentation standards and expand model validation teams by several-fold to cope with the regulatory demands of providing evidence of a specific and compelling challenge.
The larger banks have at great cost used teams of consultants from the Big-Four and to address cost constraints, have created off-shore locations. While they have addressed many issues brought on by regulatory reviews such as SREP and TRIM, the ongoing cost of ownership is now proving a huge burden for them.
Opportunity for Smaller Banks
Smaller banks struggle to attract and retain seasoned hands and junior quants, and their larger competitors continue to disrupt by hoovering up of talent. They also have the perception that the Big-Four fails to provide them with the A-Team, even though they charge a premium for their services. Out-sourcing validation is an option, but the need to keep overall assessments in-house reduces cost benefits.
The view from the Works division of Quant Foundry is that the banking industry is reaching “Peak Quant” and that the pressure on talent acquisition is starting to subside. Quants see smaller banks as a more attractive place to work as they can introduce more creative solutions rather than adopting old approaches or checking another quant’s work.
Smaller banks can find themselves with resource gaps that come from lumpy demand from an acquisition or as they start on the IRB process. It is not efficient nor practical to have full-time, under-utilised quants who quickly get bored and leave and outsourcing does not allow the build-up of skills in-house. Smaller banks have the opportunity not to repeat the mistakes of the larger banks and deliver a model risk framework from day one that is proportional and not over-compensating.
How Quant Works Supports our Clients
At Quant Works, we work with our smaller bank clients to address not just the resource gaps, but to help them build a proportionate and functioning model risk capability from the start that can evolve into a source of sustainable competitive advantage. We provide seasoned quants who can operate maturely with our clients deployed between three and six months and can help to upskill other team members.
An individual or a small on-shore team from Quant Works are more impactful and can deliver a more cost-effective solution through rapid design and delivery. Seniors that know what “good” looks like regarding model risk governance and can help with a design of model risk artefacts that reflects model complexity and not adopting guidelines that are an over-kill.
Quant Foundry
Dr Chris Cormack and David Kelly formed Quant Foundry in 2018 with the understanding that our success depended on our ability to attract and retain talent.
Through our unique control framework, each senior quant has access to our collaborative and multi-sector network of problem solvers that support and challenge solution choices as well as a quality assurance “check-in, deliver, check-out” framework that enforces consistent, high-quality delivery.
Rather than have a costly “bench”, our Quant Labs division runs several research-based and highly creative initiatives inside and outside of finance where our senior practitioners expand their knowledge base as well as supervising our junior PhD talent pool.