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Basel III Greenhorn – Process and Information System Metamorphosis

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This paper discusses the basic process and system architecture required to combat the Basel n+1 syndrome, and how technology can be creatively leveraged for active risk management.

This paper discusses the basic process and system architecture required to combat the Basel n+1 syndrome, and how technology can be creatively leveraged for active risk management.

Published in: Economy & Finance, Business

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  • 1. White Paper The Basel III Greenhorn Process and Information System Metamorphosis - Vikram Srinivasan Abstract With Basel II proving ineffective in preventing the crisis and in many ways, coupled with the compliance attitude to risk management, even responsible for accentuating it, its successor, Basel III brings with it an intention to prevent similar instances in the future. It did, however, demonstrate an unclear understanding on the regulator’s part, of what caused such untoward events. The possible recurrence of such incidents, the expectation of further iterations to Basel, combined with the need to adapt and also move up the active risk management trajectory, provides further case for organizations to examine and embrace scalable process and information system architecture. Technology is only limited by imagination and the business perception of its requirements to manage risk. This paper would combine the facets of establishing the basic process and system architecture to combat the Basel n+1 syndrome and the creative use to technology for active risk management. www.infosys.com
  • 2. The Basel II Era Up until the credit crisis of ’07, Basel was just another regulation in the compliance paradigm and hence always approached with such mindset – “Compliance”. It was often thought to be a magical rule book, by adhering to which the banks may do away with the bothers of risk. With regulatory pressures in adoption and the strict timelines, it became yet another sibling in a product vendor’s portfolio, who had transformed the rule book into a technology solution. While it was one thing that Basel was often thought to be more a ‘capital adequacy’ framework than one of risk management, the product approach made it more an implementation / technology exercise; thus ignoring the whole process, management and supervisory framework that it demanded. For another, the inherency of risk in every business transaction and hence the need and awareness for it to be managed in a decentralized manner was not envisaged to a greater degree. A risk culture was not created thereby neither rewarding measured risk nor punishing its undertaking in absurd degrees. Even as a purely technology exercise, ‘traditional products’ were not the right option in the risk management realm. These Basel products rely on the accord which has become reactive. Given that Black Swans cannot be predicted, the scalability and agility of the technology solutions becomes a critical factor, even assuming that regulatory compliance is the only mandate. Owing to the recent financial environment, active risk management is moving further up the agenda, emphasizing the aforesaid expectations from technology. While specific business needs often demand customized technology solutions, it also invariably demands involvement from the business to identify and define what is actually needed. However, the commonality of business practices across the industry gave rise to canned products or COTS. They were intended to break middle ground between the benefits offered by scratch development and faster time to market by customizing the vanilla product offering for differing requirements. But, are they good enough for the black swan argument? Given the current state, it may be a safe assumption that; those financial institutions treading the traditional products path may have limited risk management architecture that may facilitate intelligence, real-time event handling / alerting or generally, any sort of active risk management.2 | Infosys
  • 3. Basel II regime through Basel III looking glass – Lehman’s folding was a result of liquidity problems from unwinding of huge derivative positions. The 30-day stressed Liquidity CoverageA business perspective Ratio; encouragement of medium to long term funding through NetTo start with, the best way to analyze Basel III is to look at what went Stable Funding Ratio; and the variety of monitoring tools do well here.wrong in the Basel II reign and whether it would have addressed the However, there are arguments implicating that the LCRs bias towardshortcomings. government bonds could hamper credit to small businesses, which is also interesting given that they are the ones who do not have accessThe reliance on credit ratings to determine the purportedly low Basel to capital markets, and hence turn to banks for fundraising, whereII capital, through Risk Weighted Assets (RWA) led to the ‘manufacture’ their ‘unrated’ status again tend to extend the ‘halo effect’.of AAA-rated CDOs backed by lousy sub-prime mortgages, whichfuelled the crisis. In Basel III, while specific problem areas in While Basel III does well on reducing foreseeable risks, it doesn’t earn the same kudos for reducing unforeseeable risks – Banks are • Risk weighting – which has been addressed through not discouraged from engineering and piling up on exotic securities - increase in risk weight for super-senior tranches of (re) which can blow up in unexpected ways. securitization products; - elimination of regulatory arbitrage between banking and trading book, by treating securitization exposures on the Technology frameworks for the transformed latter on par with the former and risk management paradigm - strengthening requirements on OTC derivatives and repos With the traditional products paradigm being ruled out, there is a through capital for MTM counterparty losses based on need for an alternate approach in the risk management arena. ADM stressed inputs, rather Credit Valuation Adjustments or ground up development is painfully slow and does not offer the necessary flexibility. Products, on the other hand, speed-up time • Quantum & quality of capital – which has been addressed to market at the compromise of waiting on the product vendor to through higher tangible common equity and capital support n+1 or make any ad-hoc changes to integrate it into the conservation & counter cyclical buffers larger risk management eco-system of the bank. This opens up theAbove points have been dealt with, the larger issue pertains to the avenue for a mid-path approach, or what is referred to as “Productconcept of risk weighting itself. This approach still urges the banks Frameworks”. This is based on two key tenets – componentization andto “find” apparently risk-free assets which can be leveraged much modularization. And these aren’t the technical terminologies, but arehigher than their riskier counterparts, leaving lot of room for financial defined exclusively in business terms.engineering. From a technology perspective, most business needs, to a greaterWhile zero risk weight assumption for AAA and AA-rated sovereigns extent can be addressed by a cogent organisation of a set of(which caused the Sovereign debt crisis), has been acknowledged as configurable components. As a rudimentary example, in a businessfaulty, yet, it has been let be. Obviously, the governments which put scenario pertinent to risk management, Basel business hierarchy,Basel III together needed the incentive of cheap borrowing. The Euro risk rating, issue remediation, LDAs and EVTs translate into the likeszone debt crisis is another instance which proves that government of simple tree builders, workflow, rules engines, analytics, reportingbonds are not risk free and mere probabilistic calculations cannot tools etc. Retaining the configuration of every element in its silo,reveal the true nature and form of risks. makes upgradeability and portability a cinch. Loosely couple these together with the business logic, standardise data access layerWhile oligopoly of rating agencies and the Gaussian Copula-powered (with say, hibernate) to make it database agnostic and factor insymbiotic growth of CDS’ and CDOs played their part in harmonised the flexibility of the UI layer, and there is a componentised productsynchronicity, the use of internal rating models brought things to a framework at hand.close. The dumbed down simplification of VaR garnered attention inexpressing and interpreting individual and firm-wide risk as a single All of these silos need not have to be developed; they can be technicalfigure for any asset class, its limitations were however forgotten. The components which have already been purchased by the bank, forassumption that the bank was in the best position to measure its own instance, a reporting or intelligence engine. ‘Shared Infrastructure’ isrisk, when coupled with VaR’s “normal”, no-extremities market, failed an undeniable value proposition. Apart from saving tons of money into pay-off giving incentive for the banks to push risk into the tails, duplicate investments, it provides the much needed business (processmaking it insignificant. Banks latched onto functions like Gaussian & system) integration that product silos can’t. If an organisationCopula function to fatten the tail, making them even riskier. Basel III happens to purchase / upgrade, say, the intelligence engine, one candoesn’t do much to take on this issue. Risk-based compensation in squeeze every penny out of it by making it available to all applications,this case proved counter-productive, further encouraging managers and also where needed, by sharing the intelligence across the board.to paint a low-risk picture. At least with intelligence, that’s how it’s really meant to be, isn’t it? And what’s more, the products remain as recent as the newest updatedThe back-stop non-risk based measure viz. leverage ratio, is a step in component.the right direction, albeit low. If the past is any indication, Lehman waslevered 31-1, whereas the current Basel III rules peg the requirementat 33-1. Ultimately, this treads on a fine line – what cost of economicgrowth is a fair price for curbing risk? Infosys | 3
  • 4. Operational Risk Analytics Intelligence Reporting Notification Management system – Engine Engine Platform Infrastructure Modules Heat-map of Historical cost Action plans Cost & worth actual, target RCSA of unattended pending of risk and residual risks Implementation risks Scenario Loss Input / output Escalation by loss Analysis / Losses by risk / Management dataset by event parameters RCA in loss LOB scenario (Eg: magnitude) forecasting Risk events Economic / Regulatory with greater Risk OPVaR / Capital Regulatory capital adequacy than expected Measurement computations Capital / Pillar 3 frequency / Optimizations reporting severity Configs / Rules / Input / output Manipulation Input / output/ Content / contact Parameters dataset by rules per presentation parameters by scenario scenario parameters scenarioThe diagram above presents a picture of componentisation within a risk management solution. For the sake of simplicity, only the operationalrisk portion of the risk management software ecosystem has been considered. On the left are the various relevant modules, while the blue boxesrepresent the technology ‘components’. Their intersection presents a sample of what information for that module would be configured on thatcomponent. The last row labelled ‘Configs / Rules / Parameters’ presents a generalised version of the type of information available within eachcomponent and has to be catered for / migrated when the component is switched from one vendor to another.4 | Infosys
  • 5. The depiction below is an organisation view of the commonality of components within and outside the risk management solution. Anillustrative module of a retail lending system is shown to coexist with / draw upon investments made for the risk management solution orvice-versa. This can be extended to various other modules within the lending system and also a whole gamut of other business systems.Besides, data from various systems existing within a component can be cross-leveraged. For instance, the estimated PD from the retail lendingsystem in the intelligence engine can be used for determining frequency / severity of retail loan losses at a LOB level for operational riskpurposes, based on customer profile attributes beyond organisational policy tolerances (which would be available as rules already withinthe intelligence engine) Operational Risk Analytics Intelligence Reporting Notification Management system – Engine Engine Platform Infrastructure Modules Heat-map of Historical cost Action plans Cost & worth actual, target RCSA of unattended pending of risk and residual risks Implementation risks Scenario Loss Input / output Escalation by loss Analysis / Losses by risk / Management dataset by event parameters RCA in loss LOB scenario (Eg: magnitude) forecasting Risk events Economic / Regulatory with greater Risk OPVaR / Capital Regulatory capital adequacy than expected Measurement computations Capital / Pillar 3 frequency / Optimizations reporting severity Estimated PD based Revision of Risk–Return on credit rating Profitability / organizational Loan Pricing optimization and its various key projected cash loan pricing contributing factors flow benchmarks Retail Lending system - ModulesWith Basel, while data and its utilization may be different, the data structure itself, is not only generic but common across organisations. This leadsto another important dimension of these product frameworks in the form of modularisation. A module may be definable as a part of the businessworkflow that can be made as a silo with definable input, operation and output, for instance, loss management and risk-controls-assessment.Armed with this additional trait, the business can go shopping not for a product framework, but for modules of product frameworks. However,that would be at a farther state in time, when these frameworks are bit widely adopted. Infosys | 5
  • 6. Operational risk as the focal pointHaving already pointed out that the process angle was not paid new products, activities, processes and systems are introducedmuch attention to; the impact on handling Operational risk is quite or undertaken, the operational risk inherent in them is subjectsevere as it relies majorly on process assessments, streamlining and to adequate assessment procedures”. These new financialestablishing preventive and detective controls. In fact many of the products (CDO, CDS) should have been evaluated for theiritems that ended up constituting the credit crisis were operational inherent risks and subjected to proper assessment andrather than credit or market. monitoring. Simply put, new products carry more risk. Hence, the models should have imposed a penalty on assets that are Given below are some of the instances that clearly indicate the complex, difficult to understand or rarely traded, which wasn’tunderlying cause of many-a-loss was neither credit nor market risks, to be.as much as they were hyped out to be. 5. Even the whole concept of sub-prime lending may be taken 1. Mortgages were “manufactured” by banks, to keep up with the to fall in the ambit of the above. downstream demand for securitized instruments, rather than creating the latter out of mortgages that had been made on But, of course there is a thin line segregating operational from “merit” others. How to separate a poor lending choice (operational) from a genuine default (credit)? How to distinguish the voracity to make 2. The above was accentuated by purely revenue-driven incentive profits or poor investment choices (operational) from sudden market structures that encouraged business to paint a low risk picture fluctuations (market)? Before this can be answered, we need to 3. The risks in the complex instruments / strategies, which was understand, who makes these decisions. More often than not, the behind much of the crisis weren’t clearly analyzed or captured person recording it is the one responsible for the loss itself – So much – CDO and CDS were sliced into and treated as ordinary bonds for decentralisation. with a set duration and interest rate; and their systemic impact The product framework approach is not only the best fit for was never clearly understood. operational risk, but also it facilitates a process based approach 4. Fundamental principles of operational risks were ignored - to ORM, which is an entire gamut of systems working like a neural “Sound Practices for the Management and Supervision of network, slightest signs of trouble sensed, impact points delineated Operational Risk” published in February 2003 clearly outlines (by process maps), damages estimated (using algos), slew of a fundamental principle: “Banks should identify and assess the preventive mechanisms kicked in (based on criticality of impact areas operational risk inherent in all material products, activities, and predicted losses) and relevant people notified. processes and systems. Banks should also ensure that before6 | Infosys
  • 7. Concluding remarksWhile Black Swans cannot be foretold, there’s no tellingif Basel III would avoid a recurrence of the slew of eventsleading to the crisis. However, for starters, componentizedproduct frameworks allow the ability to start fromcompliance and inch towards building it up into one foractive risk management. For others, already treading thispath, these would help accentuate the process and alsomake it advanced and flexible. And, for both, given theirvery nature, these product frameworks would avoid havingto worry about losing focus on developing advanced riskmanagement capabilities and reprioritizing for complianceto Basel n+1.Extensive configurability, ability to leverage existing ITinvestments, knack of jazzing up on upgrades to theleveraged, infrequent need for enhancements to the ‘core’,elimination of vendor dependency, variety of interfaces; allclearly point to these componentized product frameworksbeing the better approach, unless the ‘unforseeable’ orBasel Accords can be foretold. About the Author Vikram Srinivasan Senior Consultant, Financial Services and Insurance, Infosys Limited Vikram specializes in process and strategy consulting with focus on asset management, risk and compliance, and private and institutional wealth management. He has successfully led and delivered several multi-year business initiatives for clients across geographies. Vikram is also credited with the conceptualization and productization of the Infosys Operational Risk Management Platform (ORM). He can be reached at Vikram_Srinivasan@infosys.com Infosys | 7
  • 8. About InfosysMany of the worlds most successful organizations rely on Infosys todeliver measurable business value. Infosys provides business consulting,technology, engineering and outsourcing services to help clients in over30 countries build tomorrows enterprise.For more information, contact askus@infosys.com www.infosys.com© 2012 Infosys Limited, Bangalore, India. Infosys believes the information in this publication is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledgesthe proprietary rights of the trademarks and product names of other companies mentioned in this document.