Capital Optimization with SAP Banking vs reporting with Central Data Hubs

 Dear,

Capital Optimization requires making visible the generation of value by processes and business segments, weighted by their capital consumption.

The General Ledger does not have the capacity to provide this analysis capacity and this is a big problem if we take into account that the General Ledger is at the center of the banking Information Systems.

Measuring the generation of value weighted by capital consumption makes it necessary to represent the organization's accounting, solvency and liquidity situation in an integrated manner, including off-balance sheet items such as commitments, derivative contracts or collateral agreements.

Integration requires the processing of large data tables and the management of large blocks of memory, because each transaction must read and update data records from other areas. This was an unapproachable challenge until a few years ago. For this reason, banking information systems were built as separate silos of information, communicated by interfaces and intermediate tables, with very little integration between them. 

Many banks are tackling this problem with the construction of Central Data Hubs with which they try to respond to the accounting and solvency regulatory reporting requirements, although in most cases with mediocre results.

Central Data Hubs receive data from all the operational, silo style, systems of the bank, homogenize the data, and finally store it in an standardized format. So it can be delivered from there to the other analytical and regulatory reporting systems of the banking landscape. 

I’ve seen several of these initiatives in the last years, and they all present common difficulties.

Project design follows an ad-hoc/on-demand approach. Data architects collect data requirements from the data consumer systems, and design the data repository according to these requirements. And then, define the interfaces for collecting data from the source systems and populating the data in the destination systems.

To some extent, this approach implies reinventing the wheel, according to the bank’s own experience, and limited integration capabilities of the bank’s information architecture.

SAP has proposed a revolutionary, holistic approach, to manage the analytical requirements of banks. This is the Finance and Risk Data Platform, which takes advantage of the SAP Integrated Financial and Risk Architecture.

The Finance and Risk Data Platform has been designed to fulfill present and future Accounting, Risk and Liquidity regulatory requirements, as an alternative to the ad-hoc/on-demand approach of the Central Data Hubs.

The Finance and Risk Data Platform combines:

- The accumulated knowledge in the design and development of the Integrated Financial and Risk Architecture of Bank Analyzer during the last 2 decades. The Primary Data Objects of the Bank Analyzer Source Data Layer as an standard template of the Operational Data, and the Result Types of the Results Data Layer as an standard template of Analytical Data. 

Data Architects take advantage of these standard templates as a basic reference, and enhance them for fulfilling the bank’s specific requirements, without breaking the integrity of the data-model.

- The high-performing capabilities of SAP HANA for storing and managing very-high volumes of data, without intermediate tables and assuring the referential integrity of the database.

- The Extract, Transformation and Loading capabilities of SAP Smart Data Integration in Premise and in the Cloud. 

- The Analytical Layers of SAP Bank Analyzer Risk Engines, Liquidity and Risk Management on HANA, Intraday Real-time Liquidity Management, etc. 

These are the guidelines followed by our team in the construction of our Capital Optimization system, built on top of the Integrated Financial and Risk Architecture.

Our Capital Optimization system speaks with the business processes of our clients' SAP Systems translating them in terms of Capital and Liquidity generation and consumption. With this information the Capital Optimization system measures the deficits and surpluses of capital and liquidity of the business processes and proposes financial instruments to offset these deficits and surpluses, optimizing the consumption of capital and liquidity of the system.

We are working on presenting our system to the market and looking for business partners and investors. If you are interested, do not hesitate to contact me at ferran.frances@capitency.com 

Looking forward to reading your opinions.

Kindest Regards,

Ferran Frances.

www.capitency.com

Join the SAP Banking Group at: https://www.linkedin.com/groups/92860

Visit my SAP Banking Blog at: http://sapbank.blogspot.com/

Let's connect on Twitter: @FerranFrancesGi

Capital Optimization and Capital Allocations with SAP Banking and Predictive Analytics

Dear,

We are in a new economic scenario, a scenario of scarcity of Capital due to the combined effect of two forces that harm the economy.

- Weak growth. The shortage of energy and other natural resources is weakening economic growth. Weak growth limits capital generation.

- Excess debt that overconsumes capital.

If capital is overconsumed and not regenerated at the same rate, capital becomes scarce and capital is the most important resource of the financial system.

Efficient management of a scarce resource requires Planning. The capital optimization process begins by planning your consumption and the expected return on the underlying investment. With this analysis we will weight the return on investment by its capital consumption, prioritizing those segments with the best performance weighted by their capital consumption.

You can find a small example in this video.

https://www.youtube.com/watch?v=GkcVF5CWVrU&t

The process continues with the monitoring of the deviations between the planning of Income and Capital Consumption with the actual results.

Portfolio Management must look to the future and future is full of uncertainty. Managing uncertainty is managing capital, taking corrective actions when reality deviates from planning above the tolerance threshold.

The tolerance level must be consistent with the assigned capital, the required guarantees and the hedging strategies. Only in this way will we be managing the portfolio holistically, as this new structural environment of capital scarcity demands.

This classical vision has acquired new potential with the development of analytical tools and machine learning.

Thanks to the integration capacity of SAP Predictive Analytics with the rest of the SAP Business Suite components, including SAP Banking, this potential is even greater, as I will try to show you in the following scenarios.

Time Series Forecast

A Time Series forecast identifies key trends that directly influence future performance. Time Series Forecasting is useful for estimating future values of a measure where we have a time dimension available to help us identify a trend. Time series predictions are always time dependent.

In a capital consumption weighted income planning process, Time Series Forecasting is very useful to estimate several critical elements such as the following:

- Future Cash-Flows.

- Default Probabilities.

- Loss Given Defaults.

- Future Collateral Values.

Depending on the magnitude to be estimated, we must select the relevant data series for the estimation. The values taken by the target variable in the past and its corresponding dates. This Time-Serie is called the signal which will be analyzed by the Time Series Forecasting process of SAP Predictive Analytics.

Data accuracy is a critical success factor and our main competitive advantage as we take advantage of the integration capabilities of SAP Bank Analyzer Credit Risk Analyzer and SAP Predictive Analytics Cloud (Source Data Layer, Data Sources, Ad Hoc Reporting, Historical Database and Open Hubs).

Classification

Classification is the process of separating data into classes. In this scenario, the target is discrete, and the goal is to separate data into groups in a meaningful way.

In Capital Optimization, classification is particularly useful for segmentation of my counterparties and financial exposures. Typically, a Bank’s portfolio is managed in a repository where each Financial Transaction and its Counterparty contains the Product, Address, Level of Studies, Industry, Collateral Type, Region, Age, etc.

Classification supports the process of separating the Financial Transactions of our Portfolio between Rating Levels and showing how particular data influencers like Industry, or Region change based on Rating Level.

In SAP Analytics Cloud Predictive Scenarios, the system analyzes data using a discrete binary variable while simultaneously considering other variables in the data set. These are known as Influencer Contributions, which show how influencers affect the ranking result.

The Credit Risk analyst can also view individual statistics for each influencer in SAP Analytics Cloud, and the software calculates all of these statistics immediately when training a ranking scenario. SAP Analytics Cloud also generates contribution graphs that visually show how each class contributes to the total data set.

This scenario is particularly useful for supporting the automatization of assigning assets to tranches in a Securitization process.

Regression

Regression Analysis is a way of sorting out which variables have the most impact in a dataset. The target is continuous in this method and is primarily used to show the relationship between two or more variables in a dataset. For example, a Credit Risk analyst would use regression analysis to determine whether there is a correlation between users’ Level of Studies and average Delinquency Level. Traditionally, this involves taking all the data, plotting it on a scatter plot for the two variables, and finding an average line if the data fits a particular trend. This would then be done for other variables that are not included in the initial regression in the traditional method. In SAP Analytics Cloud, the Credit Risk analyst would simply pick one variable and the software automatically generates regressions, complete with relevant statistical data, for all variables in the dataset and how they relate to the target variable.

Conclusion

The methodology is much more efficient when we combine the three families of algorithms and build models in which we establish the dimensions of study and those that have a more direct influence on them. For example.- The level of education has a direct influence on the level of income, and this, together with the volume of expenses, influences the Delinquency Level. This approach provides a more robust model than another in which we analyze how the level of education impacts the Delinquency Level, without taking into account the volume of expenses. 

In our case, we have dedicated the last 12 years to building Credit Risk models that take advantage of the information contained in the SAP Systems of the non-financial economy. 

These models, built on SAP Banking systems, are integrated with the business processes of our clients' SAP Systems to Plan, Segment and Monitor the Capital Consumption of their business processes.

Even more, our proposal measures the Capital and Liquidity consumed and generated by the processes of the real economy managed with SAP Systems, detecting the deficits and surpluses of capital and liquidity of the process. With this information, it proposes financial instruments to offset these deficits and surpluses, optimizing the consumption of capital and liquidity of the system.

We are working on presenting our system to the market and looking for business partners and investors. If you are interested, do not hesitate to contact me at ferran.frances@capitency.com 

Looking forward to reading your opinions.

Kindest Regards,

Ferran Frances.

www.capitency.com

Join the SAP Banking Group at: https://www.linkedin.com/groups/92860

Visit my SAP Banking Blog at: http://sapbank.blogspot.com/

Let's connect on Twitter: @FerranFrancesGi

Ferran.frances@capitency.com