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Risk management. Risk systems for Central Banks.

Risk management. Risk systems for Central Banks.

If you were tasked to build an ideal risk management system for a central bank, what would you look for? To answer this question you have to ask a different question first.

Figure 1 Risk Management. Risk Software – Central Banks

If you are a central bank in Europe, Middle East, Far East, or Central Asia, which of the following is your biggest challenge?

a. Issuing guidelines to regulated banks on regulatory reporting and risk management.

b. Making policy decisions that have long term economic and market impact.

c. Collating market data and creating an economic profile of the nation that creates visibility, sets benchmarks for investors and stakeholders; allows policy decisions to be measured and evaluated objectively.

d. Taking corrective action on items that impact and change behavior of banks and their customers in the country to ensure that key metrics such as the liquidity, private sector credit, inflation, savings, income mobility and velocity of money remain within their pre-determined ranges.

e. Implementing banking regulation guidelines and best practices based on BIS recommendations and guidelines.

The right answer would be a mix of all of the above. But the mix of all of the above relies on one crucial element that central banks all over the world collect by the megabytes, but can’t do much with.

One word. Data.

One Application. Meaningful Metrics.

Risk management for Central Banks – Ideal End State

Here are some queries that arise on a regular basis if you are a central bank regulator in any part of the world that you can answer (with ease) if you have data and meaningful metrics.

If you don’t have data and meaningful metrics each of the answers could take a few months of allocated resources, bandwidth and time and an Excel spreadsheet crafted manually.

a) What is a sector specific default rate for an SME customer, a Middle Market customer, a Corporate or Commercial customer. How much bank capital should be allocated against this rate? How does this rate vary across geographies, business sectors and segments?

b) Against the default rates identified above what is the average loss given default? The average recovery rates? What is the transition rate between non performing loan classifications?

c) What is the distribution of collateral and guarantees? If a wave of default, foreclosures and forced sales flows through our system, which asset class will suffer the most? What would be the impact of depressed values on the national economy? On credit growth?

d) What kind of sector or segment specific incentives can we create to ease the pain and rekindle growth?

e) If we raise or lower the policy interest rates by 50 basis points, what will be the impact on banking balance sheets? On PnL? On recovery rates and non performing loan classification?

f) If we increase or reduce the Forced Sale Value (FSV) Benefit, how much flexibility can we give to regulated banks in the banking system.

Risk management for Central Bank – Context.

The list of questions is infinite, but the cost of a system that answers many of these questions is not. A centralized analytics driven data warehouse for central banks has been the holy grail of reporting for over two decades now but the dream remains unrealized.

The reason is simple. You need to think like a central banker and then integrate at least 7, possibly 9 different themes and systems to get to the final warehouse design. You need to marry business, technology, banking, risk, analytics, usability & data together to get to the right reports. There are about 200 people in the world who can do this and they are all well placed and compensated enough to not get their hands dirty with selling a technology solution or even worse implement it.

The right mix of resources required to do so includes a selling team comfortable with institutional selling, an implementation partner with influence, patience and resources, a technology team that gets the domain, and an architectural design that realizes the amounts of data a central bank receives and needs to analyze to deliver on the promise of this solution.

Getting all of them together at the same time and getting them to agree on an RFP issued by a central bank is an impossible task. If you can crack it, it is a problem worth solving.