Why Systemic Financial Crises Are a Broad Failure of Risk Management
Various posts in this blog have catalogued the practice of risk management in the financial services industry. To recap briefly, the Great Financial Crisis (GFC) of 2008 was a systemic failure that brought about large scale banking losses across the globe. Considered by many economists to be the worst economic crisis since the Great Depression [1], it not only precipitated the collapse of large financial institutions across the globe but also triggered the onset of sovereign debt crises across Greece, Iceland et al. Years of deregulation & securitization (a form of risk transfer) combined with expansionary monetary policy during the Greenspan years, in the United States, led to the unprecedented availability of easy consumer credit in lines such as mortgages, credit cards and auto. The loosening of lending standards led to the rise of Subprime Mortgages which were often underwritten using fraudulent practices. Investment Banks were only too happy to create mortgage backed securities (MBS) which were repackaged and sold across the globe to willing institutional investors. Misplaced financial incentives in banking were also a key cause of this mindless financial innovation.
The health of entire global high finance thus rested on the ability of the US consumer to make regular payments on their debt obligations – especially on their mortgages. However, artificially inflated housing prices began to decline in 2004 and the rate of refinancing dropped, the rate of foreclosures assumed mammoth proportions. Global investors begin to thus suffer significant losses. The crisis assumed the form of a severe liquidity crunch leading to a crisis of confidence among counter parties in the financial system. Global & National Regulatory Authorities had to step in to conduct massive bailouts of banks. Yet stock markets suffered severe losses as housing markets collapsed causing a large crisis of confidence. Central Banks & Federal Governments responded with massive monetary & fiscal policy stimulus thus yet again crossing the line of Moral Hazard.Risk Management practices in 2008 were clearly inadequate at multiple levels ranging from department to firm to regulatory levels. The point is well made that the while the risks that individual banks ran were seemingly rational on an individual level however taken as a whole, the collective position was irrational & unsustainable. This failure to account for the complex global financial system was reflected across the chain of risk data aggregation, modeling & measurement.
The Experience Shows That Risk Management Is A Complex Business & Technology Undertaking…
What makes Risk Management a complex job is the nature of Global Banking circa 2016?
Banks today are complex entities engaged in many kinds of activities. The major ones include –
- Retail Banking – Providing cookie cutter financial services ranging from collecting customer deposits, providing consumer loans, issuing credit cards etc. A POV on Retail Banking at – http://www.vamsitalkstech.com/?p=2323
- Commercial Banking – Banks provide companies with a range of products ranging from business loans, depository services to other financial investments.
- Capital Markets – Capital Markets groups provide underwriting services & trading services that engineer custom derivative trades for institutional clients (typically Hedge Funds, Mutual Funds, Corporations, Governments and high net worth individuals and Trusts) as well as for their own treasury group. They may also do proprietary trading on the banks behalf for a profit – although it is this type of trading that Volcker Rule is seeking to eliminate. A POV on Retail Banking at- http://www.vamsitalkstech.com/?p=2175
- Wealth Management – Wealth Management provide personal investment management, financial advisory, and planning disciplines directly for the benefit of high-net-worth (HNWI) clients. A POV on Wealth Management at – http://www.vamsitalkstech.com/?p=1447
Firstly, Banks have huge loan portfolios across all of the above areas (each with varying default rates) such as home mortgages, credit loans, commercial loans etc . In the Capital Markets space, a Bank’s book of financial assets gets more complex due to the web of counter-parties across the globe across a range of complex assets such as derivatives. Complex assets mean complex mathematical models that calculate risk exposures across many kinds of risk. These complex models for the most part did not take tail risk and wider systemic risk into account while managing risk. Secondly, the fact that markets turn in unison during periods of (downward) volatility – which ends up endangering the entire system. Finally, complex and poorly understood financial instruments in the derivatives market had made it easy for Banks to take on highly leveraged positions which placed their own firms & counter parties at downside risk. These models were entirely dependent on predictable historical data which never modeled “black swan” events. That means while the math may have been complex, it never took on sophisticated scenario analysis into account.
Regulatory Guidelines ranging from Basel III to Dodd Frank to MiFiD II to the FRTB (the new kid on the regulatory block) have been put in place by international and national regulators post 2008. The overarching goal being to prevent a repeat of the GFC where taxpayers funded bailouts for managers of a firm – who profit immensely on the upside. These Regulatory mandates & pressures have begun driving up Risk and Compliance expenditures to unprecedented levels. The Basel Committee guidelines on risk data reporting & aggregation (RDA), Dodd Frank, Volcker Rule as well as regulatory capital adequacy legislation such as CCAR are causing a retooling of existing risk regimens. The Volcker Rule prohibits banks from trading on their own account (proprietary trading) & greatly curtails their investments in hedge funds. The regulatory intent is to avoid banker speculation with retail funds which are insured by the FDIC. Banks have to thus certify across their large portfolios of positions as to which trades have been entered for speculative purposes versus hedging purposes. The impact of the Volcker Rule has been to shrink margins in the Capital Markets space as business moves to a a flow based trading model that relies less on proprietary trading and more on managing trading for clients. At the same time risk management gets more realtime in key areas such as market, credit and liquidity risks.
Interestingly enough one of the key players in the GFC was AIG – an insurance company with a division – FP (Financial Products)- that really operated like a Hedge Fund by looking to insure downside risk it never thought it needed to payout on.
Which Leads Us to the Insurance Industry…
For the most part of their long existence, insurance companies were relatively boring – they essentially provided protection against adverse events such as loss of property, life & health risks. The consumer of insurance products is a policyholder who makes regular payments called premiums to cover themselves. The major lines of insurance business can be classified into life insurance, non-life insurance and health insurance. Non life insurance is also termed P&C (Property and Casualty) Insurance. While insurers collect premiums, they invest these funds in relatively safer areas such as corporate bonds etc.
Risks In the Insurance Industry & Solvency II…
While the business model in insurance is essentially inverted & more predictable as compared to banking, insurers have to grapple with the risk of ensuring that enough reserves have been set aside for payouts to policyholder claims. It is very important for them to have a diversified investment portfolio as well as ensure that profitability does not suffer due to defaults on these investments. Thus firms need to ensure that their investments are diverse – both from a sector as well as from a geographical exposure standpoint.
Firms thus need to constantly calculate and monitor their liquidity positions & risks. Further, insurers are constantly entering into agreements with banks and reinsurance companies – which also exposes them to counterparty credit risk. From a global standpoint, it is interesting that US based insurance firms are largely regulated at the state level while non-US firms are regulated at the federal level. The point is well made that insurance firms have had a culture of running a range of departmentalized analytics as compared to the larger scale analytics that the Banks described above need to run. In the European Union, all 27 member countries (including the United Kingdom) are expected to adhere to Solvency II [2] from 2016. Solvency II replaced the long standing Solvency I – which only calculates capital for underwriting risk. Whereas Solvency I calculates capital only for underwriting risks, Solvency II is quite similar to Basel II – discussed below – and imposes guidelines for insurers to calculate investment as well as operational risks.
There are three pillars to Solvency II [2].
- Pillar 1 sets out quantitative rules and is concerned with the calculation of capital requirements and the types of capital that are eligible.
- Pillar 2 is concerned with the requirements for the overall insurer supervisory review process & governance.
- Pillar 3 focuses on disclosure and transparency requirements.
The three pillars are therefore analogous to the three pillars of Basel II.
Why Bad Data Practices will mean Poor Risk Management & higher Capital Requirements under Solvency II
While a detailed discussion of Solvency II will follow in a later post, it imposes new data aggregation, governance and measurement criteria on insurers –
- The need to identify, measure and offset risks across the enterprise and often in realtime
- Better governance of risks across not just historical data but also fresh data
- Running simulations that take in a wider scope of measures as opposed to a narrow spectrum of risks
- Timely and accurate Data Reporting
The same issues that hobble banks in the Data Landscape are sadly to be found in insurance as well.
The key challenges with current architectures –
- A high degree of Data is duplicated from system to system leading to multiple inconsistencies at the summary as well as transaction levels. Because different groups perform different risk reporting functions (e.g Credit and Market Risk) – the feeds, the ingestion, the calculators end up being duplicated as well.
- Traditional Risk algorithms cannot scale with this explosion of data as well as the heterogeneity inherent in reporting across multiple kinds of risks as needed for Solvency II. E.g Certain kinds of Credit Risk need access to around years of historical data where one is looking at the probability of the counter-party defaulting & to obtain a statistical measure of the same. All of these analytics are highly computationally intensive.
- Risk Model and Analytic development needs to be standardized to reflect realities post Solvency II. Solvency II also implies that from an analytics standpoint, a large number of scenarios on a large volume of data. Most Insurers will need to standardize their analytic libraries across their various LOBs. If Banks do not look to move to an optimized data architecture, they will incur tens of millions of dollars in additional hardware spend.
Summary
We have briefly covered the origins of regulatory risk management in both banking and insurance. Though the respective business models vary across both verticals, there is a good degree of harmonization in the regulatory progression. The question is if insurers can learn from the bumpy experiences of their banking counterparts in the areas of risk data aggregation and measurement.
