Capital Optimization and the Business Case for SAP Bank Analyzer.

Dear,

Some weeks ago, I had the chance to speak with a reader of this blog, who holds a senior position in an Investment Fund.
He said “literally” that the new era of Banking is going to be dominated by Capital, as the previous era was dominated by Debt. An alternative way of saying that we’re moving from a Financial System driven by Volume to a Financial System driven by Capital Optimization.

According to some estimates, European Banks may face a Capital gap of $128 Billion with the implementation of the new regulation drawn by the Basel Committee on Banking Supervision.

https://www.bloomberg.com/news/articles/2017-04-13/european-banks-may-face-128-billion-capital-gap-as-basel-bites

Rising this Capital is going to have a profound impact on the European Banks’ profits and return on equity. Capital determines the capacity of a Bank for Lending or Investing; it is the main resource for supporting Bank’s activities.

As the Banks’ main resource (Capital) becomes scarce, the only alternative is managing this resource efficiently, which brings the question: how can we optimize the consumption of a critical resource?
Optimizing the consumption of a critical resource is a two steps activity:

1) Measuring accurately the consumption of the critical resource.

2) Planning and simulating the consumption of the critical resource under different conditions.

Bank Analyzer offers multiple advantages in the accurate measurement of the banks Capital consumption.

- Risk Exposures are evaluated individually.

- Bank Analyzer offers standard integration with the Operational Banking system, avoiding data mismatching and Operational errors.

https://www.linkedin.com/pulse/reducing-operational-costs-sap-banking-standard-ferran-frances

- Risk dimensions can be freely determined and assigned to the risk exposures, facilitating the multi-factor analysis of the portfolios behavior in stress-scenarios.

Additionally, the high-performance of the SAP HANA In Memory Database facilitates fast calculations of the Risk Weighted Assets and Capital Consumption, under multiple sets of planning data. Fast computing is the prerequisite for running simulation scenarios.

If the new banking era is driven by Capital efficiency, there’s no more critical activity than preparing the organizations for the systemic change.
For most people, SAP Bank Analyzer is the SAP component for preparing the regulatory reporting of a bank; this is a limited vision of SAP business case.
How do we want to sale a Bank Analyzer project if the message is:

“With SAP Bank Analyzer you’re going to report how well or how bad the bank performed in the last quarter.”

Is this management or  should we do better?
Management requires planning, optimization and strategic alignment.

With Bank Analyzer, we can establish the foundation of an IT Architecture for providing answers to the most important concerns that bankers and  regulators have in the new system.

- What’s the profit I expect to make with this strategic plan?

- How much capital will be consumed with this strategic plan?

- How much liquidity will be consumed?

This is the true value proposition of Bank Analyzer, and sooner than later it will be recognized.

Looking forward to read your opinions.

Join the SAP Banking Group at: http://www.linkedin.com/e/gis/
Visit my SAP Banking Blog at: http://sapbank.blogspot.com/
Let's connect on Twitter: @FerranFrancesGi

Kind Regards,
Ferran Frances.

Reconciling IFRS and Basel III with the Integrated Financial and Risk Architecture of SAP Bank Analyzer.

Dear,

As we commented in previous blogs, we’re in the middle of a systemic transformation; from a Financial System based in Volume to a Financial System based in efficient management of Capital.
And in a globalized Financial System, efficient Capital Management requires a commonly accepted regulatory framework, for measuring the Capital consumed by bank’s assets.

Today’s main sources of regulation for banks are; the International Accounting Standard Board (IFRS), and the Basel Committee on Banking Supervision (Basel III).

http://www.ifrs.org/About-us/IASB/Pages/Home.aspx

https://www.bis.org/bcbs/

The main responsibility of the BCBS is establishing the Capital Requirements for assuring the financial stability of the banking system, while the main responsibility of the IASB is establishing Fair Valuations of the assets.

Actually, both organizations are looking at the same problem, of measuring Capital consumption, from different perspectives.

- IFRS. The Fair Valuation of a Financial Assets, determines the provisions which adjust the Nominal Value of the Asset to a Fair Value, which includes the “Cost of Risk”.

- Basel III. The Capital requirements of an asset determine the capital consumed by investing (or lending).

It sounds reasonable to establish some level of reconciliation between the two approaches.

Basel III requires Banks to accumulate Capital during the expansion phase of the economic cycle, to cope with potential losses during the contraction phase of the economic cycle. These Countercyclical capital requirements are not linked to any particular loan, so they are Generic.

On the other hand, the International Financial Reporting Standards establishes the provisions, that banks must recognize, for covering the losses on their portfolio, due to events which have already happened and will affect future cash flows.

Part of these losses, come from detected failed loans, but others come from failed loans that we’re aware that exist in the portfolio, but we haven’t detected yet. For that reason, we have to evaluate the whole portfolio and adjust its value globally, also with the format of a Generic Provision.

But the problem remains, how to determine the Fair Provision for a non-visible failed loan?

An interesting approach for determining the value of these generic provisions, utilizes the Internal Ratings-Based Approach of the Credit Risk Calculation (Basel III).

For the IRB Credit Risk Calculation, we have to evaluate several components; the Probability of Default (PD), the Loss Given Default (LGD), the Exposure at Default (EAD) and the maturity of the contracts (M).

Additionally, the IRB approach let us calculate the expected losses of the portfolio (EL), which is the expected loss for every loan that we can calculate with the following formula:

EL=PD*LGD*EAD

As a driver of our reconciliation exercise, we're using the concept of Expected Losses of IRB, which is close to the concept of Incurred Losses of IFRS but not exactly the same.

The Expected Losses of the IRB approach is the average flow of losses that the internal rating calculation methods forecasts that is going to materialize in one year, while the Incurred Losses of the IFRS is the stock of existing losses of the portfolio at any given time, due to events in the past which will generate losses in the future.

Both, Incurred Losses and Expected Losses are different from the yearly manifested losses (flow of yearly defaults) and consequently the yearly flow of specific provisions.

Nevertheless, we can calculate the Incurred Losses according to the IFRS, by estimating the yearly flow of expected losses, and the time from the event which makes the loan failed, and the time when the failed loan becomes visible. This period between both events is called Loss Identification Period (LIP).

For instance if the counter-party losses his job, becoming incapable of fulfilling his payment obligations 18 months later, the Loss Identification Period would be 18 months.

Consequently if we know both magnitudes (the Expected Losses and the Loss Identification Period) we can estimate the Incurred Losses multiplying both.

For example, if the calculated Expected Losses of our portfolio (IRB Approach) are 45 million dollars/year and the average Loss Identification Period is 2 years, that means the Incurred Loss in our portfolio is 90 million dollars.

Incurred Losses (IFRS) = Expected Losses (IRB Approach) * Loss Identification Period

On the expansion phase of the economic cycle the Loss Identification Period is longer due to the easiness for refinancing policies supported but the good economic conditions.

And according to the formula the longer Loss Identification Period will make the Incurred Losses higher during the expansion phase.

This way, we’re reconciling the calculation of the IFRS Generic Provisions with the counter-cyclical capital buffer, requested by Basel III

Bringing the above method to the management of a real bank’s portfolio, requires an integrated Accounting (IFRS) and Risk (Basel III) management system, in a holistic data-model.

This is the foundation of the Integrated Financial and Risk Architecture of Bank Analyzer.

And this is what makes it the best system for measuring and optimizing the capital of a bank.

Looking forward to read your opinions.
Join the SAP Banking Group at: http://www.linkedin.com/e/gis/
Visit my SAP Banking Blog at: http://sapbank.blogspot.com/
Let's connect on Twitter: @FerranFrancesGi

Kind Regards,
Ferran Frances.

Accepting the Systemic Change. From a Financial System based in Volume to a Financial System based in Capital Optimization.

Dear

Some months, ago most of European banks complained that the low interest policy of the ECB was damaging their profitability.

https://www.bloomberg.com/news/articles/2016-06-01/european-banks-feel-the-pinch-from-draghi-s-negative-rates

But today we read that if the ECB reduces the stimulus, this will increase dramatically the risk of their bad loans.

http://www.reuters.com/article/us-eu-banks-regulations-idUSKBN16M23T

In conclusion, the problem is not low or high interest rates, because this has never been a liquidity problem. This a solvency problem due to Capital scarcity.

But assuming this has consequences; because it represents a Systemic Change, from a Financial System based in Volume to a Financial System based in Capital Optimization.

We'll talk about them in a future blog.

Looking forward to read your opinions.

Join the SAP Banking Group at: http://www.linkedin.com/e/gis/92860

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

Let's connect on Twitter: @FerranFrancesGi

Kind Regards,

Ferran Frances.

Artificial Intelligence and Financial Chatbots with SAP Banking.

Dear,
As the Financial System passes the transition period; from a model based in Volume to a model based in efficient Management of Capital, Banks are pushed by two main forces; cost reduction and innovation.

Some weeks ago, we could read that Bank of America has opened several branches without employees.

http://www.reuters.com/article/us-bank-of-america-idUSKBN15M2DY

This is just the beginning, as technology evolves, new IT-supported interaction channels between banks and clients will be implemented, reducing costs and improving efficiency.

One of the most innovative technologies are the Financial Chatbots, computer systems capable of conducting conversations via auditory or textual methods.

Financial Chatbots have existed for more than 20 years, but new computing capabilities have brought a new concept on conversational interaction with computers .

While traditional Chatbots were based on a set of rules, taking actions and giving advises by selecting options in an predetermined decision tree; newest Financial Chatbots support their decisions in Artificial Intelligence algorithms.

- Financial Chatbots based on rules provide limited functionalities, offering answers and questions according to sets and sequences of deterministic rules.

- Financial Chatbots supported by Neural Networks understand the context of the conversation, and learn from conversations they have with people.

What do Financial Chatbots represent in the Technological Architecture of a Bank’s Information System.

Financial Chatbots, in combination with the development of other interaction technologies, offer a great potential of cost reduction and customer service. But the limits of this technology will be determined, not by doing the same activities automatically, but by facilitating new value propositions.

The best example is Google, which just two years ago, introduced a new system for generating responses to search queries, with the self-learning capacities of the Artificial Intelligence; replacing the search engine algorithm that made the company successful.

https://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines

The key word is relevancy; Bank’s will only take advantage of the full potential of their Artificial Intelligence initiatives, if their information systems are capable of capturing, storing and managing all the relevant data.

This is a serious challenge, as banks information systems are supported by Silo style Information Systems; poorly integrated, and with limited capacity of crossing information by processes, products, requirements, client’s profile, etc.

Lifting this limitation requires:
- An integrated data-model, capable of providing relevant data that the Analytical Information System can process, feeding the Artificial Intelligence System.

- A High Performance Predictive Technology supporting the learning process of the the Artificial Intelligence System.

SAP Banking provides the technological architecture for tackling both requirements:

- An integrated data-model, including client’s experience, holistic business partner description, operational processes, solvency, profitability and accounting.

- The SAP HANA Predictive Analysis Library for supporting Artificial Intelligence developments.

With this two technological pillars, SAP Banking can provide the foundation of a new generation of  “Smart Banks”; taking advantage of the he Artificial Intelligence revolution, not only in client interaction, but in most of the bank’s core activities.

For instance, recently, we’ve participated in a research initiative for evaluating the potential of the Neural Networks in solving Capital Adequacy Requirements simulations.

Imagine the potential of translating the result of these simulations in marketing campaigns, private banking activities or risk mitigation initiatives.

We’re just at the beginning of the Artificial Intelligence revolution, we’ll look at other examples of this revolution in a future blog.

Looking forward to read your opinions.

Join the SAP Banking Group at: http://www.linkedin.com/e/gis/92860
Visit my SAP Banking Blog at: http://sapbank.blogspot.com/
Let's connect on Twitter: @FerranFrancesGi

Kind Regards,
Ferran Frances.

Fulfilling Banks' Integrated Reporting Dictionary regulation with SAP Bank Analyzer.

Dear,
On May the 18th, 2016 the European Central Bank published the REGULATION (EU) 2016/867 OF THE EUROPEAN CENTRAL BANK on the collection of granular credit and credit risk data (ECB/2016/13)

This regulation is commonly known as the Banks' Integrated Reporting Dictionary (BIRD).

https://www.ecb.europa.eu/ecb/legal/pdf/celex_32016r0867_en_txt.pdf

The main objective of the BIRD is defining a common framework of the data and  transformation rules which banks may implement in their Information System to fulfill the reporting requirements of the regulatory authorities.

In words of the President of the ECB, Mr Mario Draghi,

“Disaggregated data are indeed necessary to identify and analyse the heterogeneity that characterises the real world. For central banks this is particularly important: to implement policy in the most effective way, we need to know how our policy actions affect all sectors of the economy. Both the challenges posed by the current economic climate for monetary and macroprudential policy, and the information required to carry out microprudential supervision by the Single Supervisory Mechanism (SSM) increase our need for granular data”.

https://www.ecb.europa.eu/press/key/date/2016/html/sp160706.en.html

Today, banks store and analyze their risk data on pools of assets with “homogeneous” characteristics, and generate provisions, measure Capital consumption, stress the contracts, and analyze their performance with the hypothesis that all the contracts of the pool present a common risk sensitivity behavior.

But, as Mr. Draghi is pointing out, this is not enough, the level of detail (granularity) that is required today is much higher than the levels of detail required before the Financial Crisis.

SAP Bank Analyzer architecture is capable of fulfilling these requirements, with two architecture pillars:

1) Contracts and Exposures are individually analyzed by the Bank Analyzer Risk Engines. Consolidation of the Results, according to the reporting dimensions, comes later.

This bottom-up approach in which all the contracts and exposures are analyzed and stressed allows a fine-grained analysis of the Risk position of the bank;  supporting the determination of the Risk Weighted Assets and Capital consumed by any potential set of reporting dimensions.

On the other hand, the system stores the required Operational Information in the Source Data Layer, and the very detailed analytical information on the Results Data Layer, opening the gate for drilling-down from the aggregated to the detailed data, and facilitating the reconciliation.

2) The high-performance capabilities of the in-memory HANA Database, provides the capacity of processing high volumes of data with response times that can’t be provided by traditional (not in-memory) databases.

I still remember, when years ago, in my first Bank Analyzer project, the client complained, because a Credit Risk calculation with Bank Analyzer took for several hours, when apparently, other products promised to provide the calculation much faster.

The reason is that SAP Bank Analyzer calculates one by one, the Risk Weighted Assets of every contract and exposure, with the detailed granularity required by the Banks' Integrated Reporting Dictionary, and mentioned by the president of the ECB.

But today, we also have the high performance capacity of the Hana in-memory database, which is capable of offering the computing power necessary for running high-granularity analysis in the same time that other systems calculate aggregated, low-granularity analysis.

As we commented months ago, when we talked about the SAP Bank Analyzer value proposition for fulfilling the BCBS 239 requirements, the Banks' Integrated Reporting Dictionary is not an isolated piece of regulation. The whole regulatory framework is been transformed, and oriented towards disclosure and capital optimization.

https://www.linkedin.com/pulse/bcbs-239-principles-sap-bank-analyzer-ferran-frances

Consequently we need a holistic approach for fulfilling the regulatory requirements.
We’ll come back to this topic in a future blog.

Join the SAP Banking Group at: http://www.linkedin.com/e/gis/92860

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

Let's connect on Twitter: @FerranFrancesGi

Looking forward to read your opinions.

Kind Regards,
Ferran Frances.

Integrated Management of Risk Exposures and Hedging Strategies with SAP Bank Analyzer.

Dear,
In a Financial System driven by Capital Optimization, reducing the Capital consumption is a priority, and one of the main activities for reducing the Capital consumption is the efficient application of risk hedging techniques.

A common mistake is confusing Hedge Management with Hedge Accounting.

Hedge Management is a Risk mitigation technique, whose foundation is mitigating the capital consumed in risk positions by using counteracting hedging transactions.

On the other hand, Hedge Accounting is an accounting concept, whose objective is reducing the volatility in the profit and loss account, of a company which is using derivatives, for hedging its risk exposures.

Effective Hedge Management requires 3 steps.

1) Accurate identification of the Gross Risk Exposures and Net Risk Exposures.

2) Selection of the Financial Instruments with capacity of Hedging the Risk Exposure.

3) Matching the Risk Exposures (Hedged Transactions) with the correspondent Hedging Transactions.

The Bank Analyzer Source Data Layer and the Reporting Capabilities of SAP HANA offer us an excellent technology framework for the efficient execution of the above steps.

In the standard scenario of the Credit Risk Module of Bank Analyzer, we use the Source Data Layer-Positions for Representing the Credit Risk Exposures, the SDL-Position must also be linked to the Financial Transaction (or Financial Instrument) which represents the Root Cause of the Risk Exposure (Normally the Commitment and Disbursement of a Financial Transaction/Instrument).

The Process and Methods Layer of Bank Analyzer will read the information provided by the Source Data Layer and it will calculate the Risk Weighted Assets, which is the foundation for determining the Capital Requirements.

But SDL-Positons can also represent other types of Risk Exposures, we just need to enhance them with additional Characteristics and Key Figures for storing the necessary data.

For Instance, a “Risk Type Characteristic” will facilitate the representation of Interest Risk exposures, and a “Nominal Value Key Figure” of the SDL-Position can represent the Nominal Value of the Interest Risk Exposure.

We still don’t have Value-at-Risk Calculation Processes in the Bank Analyzer-PML which would provide a commonly accepted estimation of the Potential Losses, but we can use the data for defining Hedging Strategies and identify the proper Hedging Transactions. The reporting capabilities of SAP HANA are the best option for this.

Traditionally, the management of Risk Exposures have been limited to Financial Investments, Financial Assets (Accounts Payable and Receivable), Financial Transactions (Commercial Paper), etc.

This is a limited vision of what Risk Exposures are; Risk Exposures belong to the World of the Facts, and they can be originated by Business Process belonging to the main company activities, strategic investments, speculative investments, etc.

Hedging Financial Instruments also belong to the world of the Facts, they’re Financial Instruments whose behavior counteracts the behavior of the Transactions that have generated the Risk Exposure.

For instance, as an Oil company refines and stores 10 Million Barrels of Oil, it gets exposed to the Volatility of the Oil Prices (Market Risk Exposure). The company, will Hedge the Market Risk with   a Sales Order of 10 Million Barrels of Oil. But as it’s Hedging the Market Risk Exposure, it will get exposed to the Default Risk of the “Sales Order Bill-To” (Counter-party). Additionally, if the Currency of the Sales Order is not the Currency of the Oil Company, it will also get exposed to the Volatility of Foreign Exchange Rate between the Company Currency and the Sales Order Currency.

But again, this is a limited vision of the reality. As the Oil company is refining and storing the  10 Million Barrels of Oil, it’s also getting exposed to the Risk that an accident destroys the facilities and pollutes the environment (Operational Risk). As a consequence, the company will suffer the losses of the destroyed facilities and the potential environmental fines.

The Oil company has two alternatives to hedge the risk;
- Signing an insurance policy with an insurance company.
- Investing in safer facilities and processes.

Deciding what’s the most efficient strategy requires estimating the expected cost of both alternatives. The first one requires Financial Capital (insurance policy fees), the second one requires Financial Capital (investing in improving the facilities and processes), and also Know-How (Intellectual Capital).

With Bank Analyzer we can manage the first alternative (traditional scenario), but we also can take advantage of  integrating Bank Analyzer with other SAP Enterprise Core Components for making possible the estimation of the expected costs of the second alternative. In my opinion this is something that other Hedge Management products can not provide.

I’m convinced that the Capital Optimization opportunities offered by SAP Bank Analyzer, combined with its integration capabilities with other SAP Products, makes it the best option.

I’ll try to give more examples in future blogs.

Join the SAP Banking Group at: http://www.linkedin.com/e/gis/92860

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

Let's connect on Twitter: @FerranFrancesGi

Looking forward to read your opinions.

Kind Regards,
Ferran.

Capital Optimization for Clearing Houses with Blockchain and SAP Bank Analyzer.

Dear,
The more the Systemic Crisis evolves, the more clear is that the new model of the Financial System will be based in the efficient Management of Capital.

Recently, the EU Regulatory body has announced that Central Counterparty Clearing Houses (CCPs) should be subject to  Recovery and Resolution proposals.

http://www.bloomberg.com/news/articles/2016-10-05/eu-readies-plans-for-clearing-crisis-the-next-too-big-to-fail

More or less at the same time, the Bank of England has announced that Clearing Houses should be subject to Stress Testing and Capital Requirements Calculations, in order of keeping a Capital buffer to cover potential losses during the Financial Instruments settlement.

http://www.reuters.com/article/boe-derivatives-clearing-idUSL5N188411

Remember that as a consequence of the 2008 Financial Crisis, it was decided that Derivatives should be traded in centralized Clearing Houses. This decision was translated to the regulatory body by the Title VII of the Dodd-Frank Wall Street Reform and Consumer Protection Act in the US, and by the European Market Infrastructure Regulation in Europe. Similar regulations have been also implemented in other jurisdictions.

https://www.sec.gov/spotlight/dodd-frank/derivatives.shtml

http://ec.europa.eu/finance/financial-markets/derivatives/index_en.htm

Now, we’re entering in a new phase of the transformation, as the regulator forces the Clearing houses to give more transparency to their Risk Exposures and holding higher Capital levels, as a buffer for potential losses on the Derivatives trading.

As Capital requirements and costs increase for the Clearing Houses, they will have to pass this cost to its counter-parties (Financial Instruments traders), and they will have to pass the costs to their counter-parties, and so on. At the end of the chain, the Capital costs will be higher to all the market participants.

The more expensive Capital is, the more incentives the market participants will have to optimize Capital and reducing its associated cost.

In the case of the Clearing Houses, we can easily identify two opportunities for Capital Optimization.

1) Reducing the settlement time. New distributed Ledger Technologies like Blockchain represent a big improvement on this. Blockchain offers Near Real Time Settlement between counter-parties; with this technology the Clearing House can reduce the time that the House is the counter-party for the market participants, and consequently reducing the Risk Exposures and the Capital cost.

The Risk for the Clearing Houses is that, the more the settlement times are reduced, the more chances are that the counter-parties settle their trades directly, killing the Clearing House business model. Anyway, this is a different matter and we’ll talk about it in a future blog.


2) With detailed and preemptive analysis of the Risk Exposures, which facilitates the efficient measurement and request of Collaterals to the market participants, and supporting the implementation of pricing strategies, escalated according to the expected Capital costs.

For supporting the analysis of the Risk Exposures, reporting the Capital Requirements and Risk Weighted Assets calculations, and Stress Testing Requirements, Clearing Houses can use the Credit Risk Module of Bank Analyzer.

Recently I had an interesting conversation with a colleague, very experienced in Bank Analyzer implementations. He mentioned that, although he agrees that Bank Analyzer can be used in non-banking organizations, the common opinion is that Bank Analyzer target should be only Banks.

I disagree, Banking regulation requesting higher Capital levels and more transparency in the reporting of the Risk exposures is a driver, which increases the Capital costs in the whole Financial System.

The Capital costs are transferred from the Banks to the other market participants, and make them look at the capital consumed in their business processes, in order of optimizing their Capital consumption.

At the same time, the regulator increases the number of companies and market participants, which must improve their Risk exposures reporting and increase their Capital levels.

Consequently, focus in Capital consumption is spreading from the Too big to fail Banks, to smaller Banks, Insurance Companies, Clearing Houses, market agents exposed to derivatives, and more.

At the end, this is the logical conclusion of a Financial System which is moving from a model based in Volume to a model based in Efficient Management of Capital.

Join the SAP Banking Group at: http://www.linkedin.com/e/gis/92860
Let's connect on Twitter: @FerranFrancesGi

Looking forward to read your opinions.

Kind Regards,
Ferran.