# Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis
## 4 Discussion: Benefits and Services Offered by the BIMA Framework
### Risk Calculations by Monte Carlo Simulations (MCS)
MCS can achieve high-performing simulations by completing all option calculations promptly and is also more capable of running higher numbers of simulations. Instead of being unable to run beyond 300,000 simulations in one go by a Gaussian CDO desktop, MCS can run up to 500,000 simulations with much better performance, such as 20 s for 500,000 simulations for MCS versus 150 s for 300,000 simulations for a Gaussian CDO experienced in high-frequency trading [59]. Integrating Monte Carlo Simulations (MCS) and Black-Scholes-Merton Model (BSM) for High-Frequency Price Simulations  
Monte Carlo simulations (MCS) can be integrated with the Black-Scholes-Merton (BSM) model to perform full and comprehensive high-frequency simulations [59; 60] so that all pricing options can be calculated under all possible scenarios and circumstances. The combined effort can ensure a more reliable and accurate approach (as opposed to using one model in isolation). Visualization and analytics can ensure that investors and stakeholders without finance or computing backgrounds can understand the interpretations of the results, as opposed to dealing with numerical computations directly. The algorithms based on the MCS and BSM can be written and the input variables are based on the market demands, investors' targets, and the trading decisions. Based on all the inputs, the integrated MCS and BSM model can perform all the stress tests and present visualization and analytics as outputs. The integrated MCS-BSM model can be used to perform the London Interbank Offered Rate (LIBOR) calculations. Inputs based on the market-defined values, the Bank of England's interest rates, the previous LIBOR rates, time to expiry, and input prices. The integrated model can be used to compute all the possible options for lending so that results can be transparent to the potential investors and lenders. As shown in Figure 6, outputs allow any banks and multi-billion global companies to understand the implications, since they can compare the rate and also decide the best time to borrow. The results in Figure 6 suggest that the loan values under $100 million under the expiry date of 30 years will get meager LIBOR rates. When the loan values go above 30 years, the LIBOR rate can be increased according to the amount of the loan and the date to expiry. The LIBOR rate can fluctuate between 2% and 10% depending on circumstances. One interesting observation is that if banks borrow more than $350 million up to 90 years of expiry, the LIBOR rate then falls to near 2%. This means that banks can effectively acquire the maximum amount of money for the most prolonged period. If they cannot afford and even go into a bust, they may get away from legal challenges, and then the toxic debts will be absorbed by the lenders. Hence, the financialization with high trading analytics can allow this type of information transparent to the public and not the secrets shared within banking. Data processing and analysis techniques were adopted based on work presented in [PERSON] [61] that we divide large data into P-Map, P-Reduce, and P-Merge. In this way, different types and sizes of complex data can be processed with a short execution time. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Lower Limit** & **MCPrice (Exact Risk Price)** & **Upper Limit** \\ \hline Put Prices: & 2.21430 & 2.22728 & 2.24039 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation of the exact risk prices for European options (time step = 10).