Risk Management in Financial Institutions


Financial management is an indispensable aspect in any business, particularly in the financial institutions. Therefore, quality and efficient gadgets are required in measuring its effectiveness. As the financial institutions grows, the financial system becomes complex due to the bureaucratic procedures the business operates in the name of trying to portray authenticity and transparency in the financial activities and transactions within the system.

In measuring the risks attributed to the financial management, models are required to help in the process of decreasing the complexity in the management of finances. The models will also help in measuring the risks associated to the finances and thus give credit to the accountants. Although the models might be very essential in controlling some of the risks in the management of the finances, on the contrary they might be exposing the institutions on the perils which become very unrealistic on what they are capable of doing.

Performance of Risk Management Models

Depending on the past history on how the financial models have been used in measuring the financial risks, every individual who has the experience of using the models disapproves them as the best parameters in financial assessment. Even the model designers do not find them efficient. At the same time most of those individuals who are against the use of the models in both for the internal risk control and for assessing general financial risks are not certain about their effectiveness. Though, one might ignore using financial models in assessing the risks, some other financial models may demonstrate a high extent of risks which even doubles the financial models (Mackenzie, 2003)

The Value –at –Risk (VaR) model is one of the common models known to expose financial institutions at stake if not well used since it gives its credentials based on assumptions and therefore may not be selected as the best decisive factor in measuring risks. The models may also portray some uncertainties after portraying their results after their performance. It is very obvious or usual that in small transactions the amount of risks involved should be small and vice versa, compared to bigger transactions. But this may turn to be the opposite when using models in determining the level or amount or risks. The areas that the business expects to realize high incomes due to the small risks involved may turn to be the opposite (Merton, 1992).

The reason as to why such incidences might occur is due to the fact that models may sometimes lack some credential instruments used in assessing the risks. This mostly happens if some transactions have been flawed right from their initial purchasing. Another critical risk occurs during the rating of various operating agencies within or outside the financial institution, since the models used do not take any responsive action on the liquidity or the pricing.

The models may therefore account for the ratings by using some exaggerated data or even use an inappropriate model in a certain field which requires a specific model. This will therefore lead to insufficiency in the checking quality of the documented data which might result to biased data. The failures in assessing the risk are not only portrayed through the general approach of the modeling but also some basic mistakes are made (Bitner, 2008).

Furthermore, estimation of correlations between personal assets is a chief problem that is being portrayed by the models. A good model for assessing the best estimation of correlations is lacking and this is an issue that is placing financial institutions at stake since the basic operations in the business will always remain flawed. Evaluations on how companies are being rated have been an issue which has been under investigations for a couple of years and the benchmark provided by the ratings compels the business to have a closer look on the quality of the ratings.

It becomes very difficult to compare the ratings of different financial institutions, bearing in mind that they use different approaches in their business and they also do not assume the same strategies. The ratings may also vary depending on the performance of the employees with regards to their competitiveness (Merton & Perold, 1993). It is therefore true that financial institutions that are surviving the financial crisis are those embracing efficient management practices and not relying on the effectiveness of models.

A risk model in the assessment of the management of financial risks is an indispensable aspect which should be embraced by financial institutions if only their limitation could be recognized. The models have been very effective in managing small business but they are not very effective in assessing business operations with large transactions, as they involve high risks. Use of models in the financial institutions may at times be thought to be curtailing management risks but they may be aggregating the problem or complicating issues. Financial institutions have been relying on the statistical models such as Gannt chart in alleviating some of the risks, though they still are not effective.

Surprisingly, financial management team might be insisting on maintaining a superlative place for the analysis of the statistical work using the financial models. The reason as to why they may be reinforcing on this aspect is because superiority is an implication of quality work performed. In attaining this objective, an effective and efficient statistical model ought to be right. This could be right if the principle laws used in physics could be similar to those used in finance, however those in finance might appear a little bit complicated. As illustrated in the Newton’s law of motion; a force applied on an object is equal to the opposite force provided the objects, assuming the same rate of change in momentum.

The theory holds and cannot change which is very different to the financial solutions, where the models used can be altered in order to come up with a correct solution. This can be explained by the following reasons;

Endogenous risks

Statistical data and properties that need to be modeled will always change and this will be triggered by the trends observed in the rational market as the participating groups will react to the information they get. This affects the performance of the models. The upshot in the financial markets represents the overall strategic behavior of the entire group in the market with diverse abilities and objectives. Financial modeling in this case will change, even the statistical laws governing it in the immediate time and therefore leaving the modelers to seize the laws required. Endogenous risks therefore becomes manifested when the financial systems is under crisis (Laeven & Levine, 2005).

Quality of Assumptions

In any financial analysis, Models are used to suppress the complexity of the financial equations into manageable factors which are easy to be worked on. The quality of the assumed risks in the financial system therefore becomes an important aspect in choosing the model to be used. This is important since the model picks what to use and leaves the non-essential ones. Quality of assumptions has therefore been an important aspect in the recent past where liquidation was a key factor which has now been ignored in the model design (Merton & Perold, 1993).

Quality of the data

The financial data analysis has always changed from time to time, being contributed and influenced by other financial variables. The short age of the financial date has also been contributed by the varying economical aspects. This can be explained by the use of the following examples:

Market risk models

In this analysis, we consider one of the simplest but possible risks within the financial institutions – the risk modeling exercise which accounts for an approximate 99% value at risk. The analysis of such kind mostly occurs in a big financial institution or corporation such as the IBM. Since the risks involved in such large organizations are large, subjective decisions ought to be made in order to minimize the risks.

The models also portray a risk in the financial evaluation and regulation as the bankers continues to use them. The increasing use of the model risks is problematic as it may create bad incentives and it may also destabilize the financial system besides creating a state interference in the finances. Financial regulation based on the models is also an associated problem since the regulators may not be able to view or understand how the model will be incorporated within the financial system because of its complexity or the past history. Though, the regulation of finances using the models has been discouraged all through by higher financial institutions due to the heterogeneity of the risks, the IMF uses the same model and finds them effective (Financial Times, 2007).

In this light, risk measurement and assessment has become a competitive issue in most of the financial institutions and this is triggered by the way the competitive institutions are able to harmonize the risks. Ultimately, it has become clear that financial regulation based on the models is a key player in enhancing healthy financial system and thus the last resort in modeling the risk.

Improving Risk Management Models

With the growing rate of financial institutions and the increase in the number of needs that are to be fulfilled, there are so many risks that crop up, which put these financial institutions at a point that they ought to have great caution. Such eventualities have raised a concern that has necessitated the formulation and amendment of models that will put such high risk businesses at a safer position. However, the models that are currently used were formed many years ago, hence due to the many changes that have unfolded and with the rate of revolution of financial institutions, these models have reached a point of being obsolete due to the fact they are not updated as time changes (Coval, Jurek, & Stafford, 2008).

This has made most businesses so suffer the risks that have developed with time hence making it necessary for the management team of these financial institutions to improve the models so as to fit the kind of system that is exiting currently. For instance, there has been a great change in the technological way of doing things due to the introduction of new advanced financial models. This has been propelled by the invention of computers. Mackenzie (2003) outlines that information has been integrated into the business world in the sense that most of the information that was conveyed or saved in hard copies is now easily done by the use of computers and other related gadgets and systems such as the Internet.

With the introduction of this kind of high level system, it has become a challenge for most institutions to adapt. The personnel of these institutions have also experience a hard time learning how to operate their activities using the new systems. This has posed many businesses with a challenge because the old system has been completely inculcated in the minds of the staff; hence most trades have succumbed to risks of improper recording of transactions using the advanced system. So many losses have been incurred due to such problems, at the same time financial institutions have been exposed to the risks of hacking and fraud (Merton, 1992, Pg150).

Another area of concern as far as the how models that are been used by financial institutions can be improved is on risk management. Some financial institutions tend to choose the option of ventures that are deemed to be of high returns but accompanied by great risks.

Such instances make the institutions to overlook the risks that are likely to occur due to the fact that they are only focused on the objective of making profits. Such businesses are susceptible to risks, in case a certain mishap occurs, since they had not set precautionary measures to help them check such occurrences. It is therefore crucial for financial institutions to be very careful as they start certain kind of investments that might make them vulnerable to risks by setting necessary precautionary measures and strategies that puts them at safer position in case of any eventuality.

Financial institutions should also improve models that evaluate and classify the kind of risks that are likely to hit the business. Most risk managers have failed in this area since they are not able to create models that appropriately identify all the type of risks that can happen and the way they can be curbed or controlled. Another area of concern is on the models that check on credit facilities and how they are managed. According to Duffie (2007), most financial institutions have suffered losses due to credit default. This is an instance that occurs when, for instance, a person is given a loan by a bank but fails to repay the credit as agreed.

This mostly happens because of lack proper scrutiny of debtor’s credit worthiness. If such requirements are not put into consideration, the debtor gets a good opportunity of defaulting. This happens because there are no legal ways that the financial institution can apply to trace the debtor, therefore making the institution to lose the money.

Such incidences have made financial institutions to see the importance of analyzing the credit worthiness of their customers who wish to be loaned some money so as to evaluate the amount of money that ought to be given to certain person, depending on the analysis done. Financial institutions should also demand that anyone who wants credit facility should have either a guarantor or a security to offer a warranty in case the debtor fails to repay the loan (Danielsson & Shin, 2002).

The nature systematic modeling has raised concern in the way it is used or how it works. It has been discovered that there has been a trend in most of the financial institutions opting to use a common risk modeling method which makes them to lack diversified means of solving problems that might occur since they all use the same remedy. It would be better if each and every institution formulated its own unique risk model since it would make them not have the likelihood of suffering the same kind of risk at the same time, hence making the market to remain stable due to the fact that other financial institutions are still working when one is under recession


In consideration of all the risks being associated with the financial models in assessing and measuring the risks, it becomes difficult to understand why the financial supervisors are still on the verge of advocating for the use the models. If the financial institution continues to be model driven, there is an insinuation that the institutions will fall and collapse since the models are now even not able to control the basic and minor risks. The financial crisis has therefore been propagated by the unreliable models and the solution of this problem may not be reached upon unless a strategic move on the models is carried out.

Reference List

Bitner, R., 2008, Greed, Fraud & Ignorance: A Subprime Insider’s Look at the Mortgage Collapse.

Coval, J. D., Jurek, J. W., and Stafford, E., 2008, Economic Catastrophe Bonds. Harvard University: Mimeo.

Danielsson, J., 2002. The emperor has no clothes: Limits to risk modeling. Journal of Banking and Finance, 26(7):1273–1296.

Danielsson, J. and Shin, H. S., 2003, Endogenous Risk. Modern Risk Management — A History. Risk Books.

Duffie, D., 2007. Innovations in Credit Risk Transfer: Implications for Financial Stability. Stanford University: Mimeo.

Financial Times. 2007. Goldman Pays the Price of Being Big. Financial Times.

Laeven, L., and R. Levine. 2005. Is there a Diversification Discount in Financial Conglomerates? University of Minnesota. Minneapolis. Unpublished working paper.

Mackenzie, D., 2003. Long-Term Capital Management and the Sociology of Arbitrage. Economy and Society 32:349–80.

Merton, R. C. 1992. Operation and Regulation in Financial Intermediation: A functional Perspective. HBS Working Paper 93-020. Cambridge, MA: Harvard Business School.

Merton,R. C. and Perold, A. F., 1993. Theory of risk capital in financial firms. Journal of Applied Corporate Finance 6(3): 16–32.

Myers, S. C. 1977. Determinants of corporate borrowing. Journal of Financial Economics 5:147–75.

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