ACTUS – A Data and Modeling Standard for Consistent and Transparent Risk Assessment as well as Straight-Through Processing for the Fintech Era

Author: Wolfgang Breymann

Fintech or, as I prefer to call it, the industrialization of the financial sector is a worldwide transformation of the financial sector with the goal to make the latest developments in ICT and data analysis available for the financial sector. It is part of the even broader process of digitization of the word and encompasses not only the development of new products and new business models but, perhaps even more profoundly, the way future financial institution will operate; it can be viewed as the industrialization of the financial world.

Automation is an integral part of industrialization. Thus, we should expect the (highly) automated bank to emerge from this industrialization process similar to automated manufacturing plant, which are mundane in the car industry. While manufacturing plants are dealing with physical things, banks are essentially dealing with data using computers. A bank can also be considered as an IT company applied to the financial sector. Still, in spite of using computers heavily, the financial industry is only just in the transition process from the pre-industrial era into the industrial one. The reason is the lack of automation and one of the impediments are missing data standards; or – more precisely – data standards that are not appropriately adapted to the use case.

Since fall 2012 my team is participating in the project ACTUS, which is the acronym for Algorithmic Contract Type Unifying Standard. The original goal of the project was to develop a data and modeling standard of financial contracts in view of financial analysis and risk assessment. This sounds (and is) quite technical and at the same time it is at the core of the functionality of a bank, namely the assessment of future financing needs and the development of the future values of a bank’s overall positions. It is important that this task can be carried out consistently, quickly and in a transparent way. Unbelievably for the non-expert, this is by far not the case today: If today the regulator demands the banks to carry out a stress test, it takes weeks if not months if the results are available, and they are not even comparable among different banks. However, it is not per se infeasible if one uses the right concepts and technology. Typically it heavily relies on simulation, which, besides experiment and theory, is a standard approach in science for understanding complex systems and establishing effective control. If based on “first principles”, simulation of a complex system needs to build on granular data, i.e. it must start at the level of the system’s basic building blocks. Business organizations and especially financial institutions can be understood just as such complex systems. Simulating their business on a granular level is a complex computational task similar to weather forecasting or other large-volume data-processing tasks. However, while the meteorological infrastructure evolved over decades in a collective effort such that today, local, regional and even global weather forecasts are common standard, financial institutions lack the risk assessment infrastructures necessary for similarly frequent and consistent risk assessments at the different levels of the system.
Partially, the lack of appropriate risk-management capabilities in the financial industry can be attributed to the fact that risk management could not keep pace with financial innovation. With the advent of financial mathematics new and highly complex financial instruments have been engineered over the course of the last two decades. On the other hand, risk management failed to take full advantage of the developments in modern ICT and still relies on (often ad-hoc and not properly systematized) analytical shortcuts and traditional technology such as Excel, which is not properly maintainable. ACTUS is providing a centerpiece to change this situation.

Atomic building blocks
The basic (quasi atomic) building blocks of a bank’s balance sheet are the financial contracts (also called financial instruments or assets). They encompass the whole financial universe reaching from widely known securities as stocks and bonds to complex derivatives. The balance sheet of even mid-size banks consists of millions of contracts. It is due to their heterogeneity as well as the fact that different risk factor categories must be treated differently as to financial valuation that consistent risk assessment is still a big challenge. Consistent risk assessment requires Monte Carlo (MC) simulations for all but the most basic derivatives to properly take into account the effect of the future price fluctuations of the underlying instruments on the derivative price.

Important in the following is that, going back to first principles, the ingredients for all kinds of financial analysis such as the valuation of a financial contract are the future expected cash flows together with the adequate discount factors. This means that the future cash flows must be evaluated at the level of the individual contracts (i.e., the granular level). This requires large IT resources as to both storage and CPU, in particular if future uncertainty is taken into account appropriately through a multiplicity of risk scenarios.

In its conception, ACTUS follows the book “Unified Financial Analysis” of Brammertz et al. (2009), which is co-authored by the author of this contribution. ACTUS supports an analytical process that can be organized in form of a data supply chain as depicted in the following figure.

Overview of the data flows in the ACTUS framework

Overview of the data flows in the ACTUS framework

The main parts are the following.

  • The input elements consisting of financial contracts and risk factors.
    • Financial contracts play the central role in this model. They consist of contract data and algorithms. The contract algorithms encode the legal contract rules important for cash flow generation (who is paying how much to whom under which circumstances) while the contract data provide the parameters necessary for the full contract specifi-cation.
    • Risk factors determine the state of the financial and economic environment. They are further divided into factors for market risk, for counterparty risk and for all the remaining risk factors lumped together in a third catch-all category called “Behavior”. The important property of risk factors is that their future state is unknown. The most important market risk factors are interest rates, foreign exchange rates, stock indices and commodity indices. Counter party risk factors typically consist of credit ratings and/or default probabilities.

In order to generate the cash flows encoded in a contract, both contract data and risk factor information is needed. The reason is that the contract rules may refer to market information such as interest rates in the case of a variable rate bond. Notice that the separation of risk factors and contracts is important because it separates the known from the unknown: the contract rules are deterministic (known) while the future development of risk factors is unknown, they have random components. The future development of the state of a contract is completely determined for a given risk factor scenario, i.e., an assumed future development of their values.

  • The raw results are cash flow streams together with some auxiliary information obtained as output of the contract algorithms. Assuming n contracts and k risk factor scenarios, there will be n x k cash flow streams consisting of 20 to 50 events each. Since there are millions of contracts on a bank’s balance sheet and a MC simulation does contain up to 10’000 risk scenarios or even more the size of the data can easily be of the order of Terabytes for large institutions and Petabytes for the whole financial system. This requires the use of Big Data technologies. Test in this direction are currently under way.
  • The different types of financial analysis such as liquidity and solvency calculations are carried out on top of the raw results. This encompasses income analysis, sensitivity analysis and different kind of risk measures. Important is the possibility to flexibly aggregate according to different criteria.

In addition to ACTUS there is another – complementary – industry initiative that aims at providing the financial industry with the framework necessary to support financial analysis on a granular level. The Global Legal Entity Identifier (GLEI), which is already in its deployment phase, provides a worldwide system for assigning unique identifiers to any entity that is the counterparty to a financial contract. Taken together with ACTUS, which aims at creating a standard machine-readable algorithmic representation of the contingent cash-flow obligations embedded in financial contracts, these two foundational initiatives provide the necessary granular data basis for consistent financial risk management.

From the software engineering point of view ACTUS is a library of Java routines that is in the process of being released as open source by the ACTUS Users Association (AUA). It cannot be used stand-alone but must be embedded in a suitable environment that adds risk factors models, manages data input and output and carries out the analytics based on the cash flow results. AUA is in the process of promoting ACTUS to be integrated by financial institutions and third party software vendors. Currently there are three ways to use ACTUS: One for pure demonstration purposes, one for prototyping purposes and one for deployment as operational system in financial institutions.

The first one, provided by the Contract Type Calculator on the ACTUS website, gives the user the possibility to enter data into a web form, provides some choices for simple risk factor models, carries out the computation and presents the results in graphical and tabular form. The website also contains more detailed information of the contracts.

The second way consists of a number of R packages bundled under the name of Risk & Finance Lab (RFL). They provide access to the ACTUS contract types and support financial modeling and analysis. We used them to carry out an ACTUS proof of concept with about 4000 real bond data, cf. Breymann et al. (2016). The package will be available soon on the CRAN server.

The 3rd way of using ACTUS is through the Ariadne Risk Management Platform, which is a professional risk management platform for financial and non-financial institutions newly developed on the basis of ACTUS.

RFL and Ariadne as well as open challenges such as using ACTUS with Big-Data technology and extending ACTUS contract types into full-fledged smart contracts by attaching them to a public ledger (blockchain technology) will be the subject of future blog contributions.

W. Brammertz, I. Akkizidis, W. Breymann, R. Entin and M. Rüstmann, Unified Financial Analysis. Chichester, 2009.

Breymann, N. Bundi, J. Micheler, and K. Stockinger, Large-Scale Data-Driven Financial Risk Assessment. In preparation, 2016



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