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Algorithmic Contract Types Unified Standards (ACTUS)

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ACTUS
Open Source, Not-for-Profit
Industry Finance
Founded 2015; 4 years ago (2015)
Headquarters Rockville, MD, United States
Products Financial Contract Software and associated Data Dictionary
Website www.actusfrf.org

ACTUS (Algorithmic Contract Types Unified Standards) establishes standards for financial contracts/instruments.  It includes data and algorithmic standards for financial contracts that together generate the contractual “cash flow” obligations of financial contracts/instruments. Such cash flow information is required for transaction processing and financial analysis. ACTUS bridges the gap between simple classification standards and functionality. The algorithms also constitute the core computations required for “smart” financial contracts.  It is developed by the ACTUS Financial Research Foundation, a 501(c)(3) not-for-profit research corporation, and promoted by the ACTUS Users Association, a 501(c)(6) not-for-profit trade association. ACTUS is able to represent virtually all financial instruments currently extant in the financial markets with not more than three-dozen ACTUS Contract Types (CT’s). These ACTUS CT's are a set of generic financial contract templates that represent the unique patterns of cash flow exchange obligations stipulated in financial contracts.

The ACTUS standard implements these obligations as machine-readable logic that allows ACTUS-enabled applications to derive both current, as well as future, cash flow obligations. ACTUS provides a common and consistent business logic for financial applications, such as transaction processing systems, bookkeeping systems, risk management and, more generally, for any type of financial analysis both, historic and forward-looking. The different aspects of the standard (the data dictionary and a description of the algorithmic representation of the CT's) and a demo application are available and explained in more detail on the ACTUS homepage.:.[1]

History of ACTUS[edit | edit source]

The vision of ACTUS grew out of the confluence of two parallel developmental paths:

  • The first was regulatory experience: Allan I. Mendelowitz, based on his experience as the chairman of the regulatory body overseeing the Federal Home Loan Banks, recognized the deficiencies of much of conventional regulatory reporting. The data did not reflect dynamic market conditions, were not forward-looking, and were not available in near time.
  • The second developmental path was based on the extensive experience of a practitioner in the area of risk management, financial forecasting, profitability analysis, IFRS book keeping, and regulation. Willi Brammertz developed the basic concept of Contract Types in his PhD thesis and subsequently, together with distinguished colleagues from academia and industry, developed a refined version in the monograph Unified Financial Analysis: The Missing Links of Finance (Wiley 2009)[2][3]; see especially chapters 3 to 6). Based on this Contract Types approach, a proprietary risk management system was developed and implemented in more than 100 institutions ranging from small regional banks to large multinational institutions and covering activities as diverse as derivatives trading and Islamic banking.

The confluence of these parallel developments occurred in the aftermath of the financial crisis of 2008. The inadequacy of financial information for critical purposes had become obvious. Policy decisions had to be made largely without the data and analytics needed to understand what was happening, as well as the consequences of key decisions. The dire consequences that followed the collapse of Lehman Brothers provide a striking example. The extent of Lehman’s interconnectedness with other major financial firms only became apparent in the aftermath of the bankruptcy filing. This experience prompted Mendelowitz to co-lead an effort[4] that resulted in the creation of the Office of Financial Research (OFR), an independent agency within the U.S. Treasury Department. The OFR legislation established new reporting authorities and mandated the creation of data standards necessary for the reporting and analysis of granular transaction and position data, a promise that however has not fully materialized yet[5]. In the early phase of these efforts, Willi Brammertz presented the notion of Contract Types to Allan I. Mendelowitz and his colleagues. Out of this encounter ACTUS was born. Shortly afterwards, Wolfgang Breymann and his research group at Zurich University of Applied Science[6], who had secured funding for the development of a flexible prototype risk assessment system for research and teaching, joined them and provided knowhow in quantitative financial modeling, as well as software design and implementation skills.

The ACTUS project has received financial support from a number of contributors including the Alfred P. Sloan Foundation, Zurich University of Applied Sciences[6], Deloitte Consulting, Stevens Institute of Technology[7], and Singular DTV GmbH. Several law firms have provided generous pro bono legal assistance.

The ACTUS Standard[8][edit | edit source]

The ACTUS standard has the following three elements:

  1. The data standard (the Data Dictionary that defines contract terms)
  2. The algorithmic standard (the logic of the Contract Types)
  3. A reference implementation

The data standard and the algorithmic standard are closely linked and should be viewed as if they are two sides of a single coin. Financial contract data are mapped onto a cash flow generating algorithm to compute a contract’s cash flow obligations. The cash flow generating algorithms cannot make any computations without the data, and the data have precise meaning only in reference to an algorithm associated with a specific contract type.  For example, the contract term “maturity date” might always indicate the end of a contract.  Nevertheless, its operational meaning can be different for different contract types. For a fixed rate bond, it is the date at which the entire principal of the bond is typically due and payable.   However, for a self-amortizing mortgage, it is the date on which the final installment of the mortgage is due.  This payment typically includes only a very small amount of principle compared to the initial amount borrowed with the mortgage.

Data Standard[edit | edit source]

The data standard defines the contract-specific terms (i.e. input parameters) needed for a contract type to generate a contract’s cash flow obligations. This includes obvious information such as maturity date, notional amount, interest rate determination, payment cycles, strikes price, etc.  However, it also includes more obscure technical information, such as day-count method and business-day convention, which is necessary for computing precise daily obligations required by operational systems. Some data elements are independent, however, others may contain dependencies that vary with each of the different contract types. For details, the reader is referred to the ACTUS home page[1], which also contains a technical specification of the data dictionary in form of a downloadable EXCEL file.

Algorithmic Standard[edit | edit source]

Financial contracts are unique in a critically important aspect when compared to the contracts used by other types of businesses. Contracts employed by other businesses typically define the terms under which goods or services are exchanged for payments.  Financial contracts are different in an important way; they consist of agreements to exchange only money in return for money. Technically, this money exchange can be fully specified by the informational elements time, currency, amount, payer, and payee (that is, who owes whom how much and when the payments are due).  Such payment obligations can be specified most precisely with mathematical formulas. Therefore, it seems logical that financial contracts would be represented by these formulas or their algorithmic representations.  However, for historical reasons, financial contracts are still mostly written in natural language. Nevertheless, the huge volume of financial contracts handled by banks requires the use of automated systems to compute and execute payment obligations. Such systems represent financial contracts as mathematical formulas and the natural language financial contracts are mapped into the automated payment systems. However, a single bank will typically have many different transaction processing systems, which lack a common standard for representing the contracts. The situation gets even more complicated and costly when the banks try to subject their contracts to the analysis needed to run their businesses. The analytical systems stand apart from the transaction processing systems and cannot use the mathematical representations available in them due to the lack of a standard representation. The entire approach tends to be error prone and is the root cause of the chaotic data situation found in the financial sector today.  This is the highly problematic and costly situation that ACTUS aims to eliminate.

Central to the ACTUS standard is the concept of Contract Type (CT). Each CT embodies a standardized cash-flow exchange pattern and includes a set of input data (contract terms) and a set of cash flow generating algorithms.

The importance of the concept of Contract Type is demonstrated in the following examples that highlight the relationship between financial contract data and the cash-flow generating algorithms. We specifically demonstrate that the terms of a contract (i.e. the contract data) by themselves are not sufficient to compute a contract’s payment obligations.  We make this point by comparing two different contracts that have the exact same contract terms, yet create very different payment obligations.  Table 1 presents the contract terms used by both Contract A and Contract B.

Table 1: Contract Terms (Data)
Contract Term Symbol Data
Issue date:      February 1, 2012
Maturity:­­ February 1, 2014
Principal N USD 1000
Interest rate IR 5%
Day count method DCM 30/360
Interest payment cycle IPCycle Semi-annually

With one type of financial contract (Contract A) we get the following payment sequence

[Figure 1]

The red arrows represent principal payments and the green arrows represent interest payments. The dashed green lines represent the accrual of interest obligations. This example represents the contract from the perspective of the lender for whom the loan is an asset. Therefore, upward pointing arrows are outflows and downward pointing are inflows (the first cash-flow payment is the principal of the loan).   

Every number in the graph can be computer generated from the initial contract terms. Interest payments, for example, are functions of the loan’s principal ($1000), the payment cycle (half yearly), the interest rate (5%), and the day-count convention (30/360). The day-count convention defines how time is measured when computing interest payments.  Under 30/360 each month is treated as if it consists of 30 days, and each half year, independent of the actual number of days counts as exactly 0.5 years. Hence, in this example each period for which interest is paid is exactly one half a year (0.5Y)

Each interest payment therefore is:

                       NT*IR*IPCycle(DCM)=$1000*0.05*0.5=$25

Most people – including finance professionals – would agree that the sequence of payments in Figure 1 (given the data of example 1) is correct. However, the cash-flow exchange pattern created by Contract A is based on an assumption about the nature of the contract itself:  Contract A is assumed to be a bond for which the principle of the bond is due and payable on the maturity date of the bond.

Contract B is a different type of contract and it produces a very different cash-flow exchange pattern, even though it has the exact same terms (data) as Contract A. In Figure 2 we graph the cash-flow exchange pattern of Contract B.  As is clear in the following graph, Contract B’s cash-flow exchange pattern is very different from that of Contact A.

[Figure 2]

The difference between Figure 1 and Figure 2 is that the two contracts represent two different cash-flow exchange patterns or, as we refer to them, different Contract Types (CTs).  Contract A, whose payment obligations are graphed in Figure 1, is a type of contract represented by the CT Principal at Maturity (PAM). Contract B, whose payment obligations are graphed in Figure 2, is a type of contract represented by the CT Annuity (ANN).

For a PAM CT “Maturity” defines the date at which the borrower is obligated to repay the entire principal.  For the ANN CT, the contract requires periodic level payments consisting of both interest and principal.  While the sum of the two components of the periodic payments (principle and interest) is fixed, the amounts of interest and principal change with each payment. Each subsequent payment includes more principal and less interest than the previous payment, adding a level of complexity to the calculations that is not present when computing payments for the CT PAM.

The composition of the CT ANN payments can be seen in Figure 2.  While the arrows that represent the total amount of each repayment on the loan are constant (the four down arrows to the right), each payment has a different proportion of interest (green arrows) and principal (red arrows).  

Other cash-flow exchange patterns could be generated, as well, with the data of Table 1 if they are used in conjunction with other CTs. We conclude, that contract terms (data) alone are not sufficient to determine the intended cash flow exchange pattern. We need both the contract terms AND the CT that embodies the intended exchange pattern of the natural language financial contract.

An overview on the ACTUS CTs is found on the website[9]. The description of the algorithms of the most important CTs can be downloaded from the ACTUS website[10]

Reference Implementation[edit | edit source]

Any natural language contract, even if very carefully written, leaves room for different interpretations when turned into computer code. Differences might stem from lack of precision, overlooked details, erroneous interpretation, or even “bugs” in the programming.

For this reason, ACTUS implements the algorithmic standard with a reference implementation available as a Java library downloadable from a permissioned Github.

Currently there are 18 CTs that are programmed, tested, and validated as official releases of the ACTUS Standard. These first 18 CTs do cover more than 80% of the contracts in use by banks and financial markets. Another dozen or so CTs is needed to complete the full set of ACTUS CTs.  They will be released and posted for downloading when their programming is complete.

The Java library is available on a fee-free basis under an open source license that is based on a widely used open source license.  However, it does contain specific additional clauses intended to protect the ACTUS standard. Users are free to use the code, include it in their commercial products, and modify it as they wish.  However, code that has been changed or altered can only be said to conform to the ACTUS standard after being approved pursuant to an ACTUS validation and approval process.

This is also the case for alternative implementations of the standard, such as versions written in other programming languages. Strict approval rules are necessary for the protection of the standard.

We encourage potential users to join the ACTUS community, download the code, further test it, and contribute to the improvement and validation of the ACTUS standard.

Benefits of ACTUS and Impact[edit | edit source]

The adoption of the ACTUS standard renders the following benefits[11]

  1. Efficiency: cross-industry, cross-department coverage of financial instruments in a highly performant[12][13] machine-readable format;
  2. Consistency: unified representation of almost all instruments for both transaction processing and analysis;
  3. Transparency: contractual terms of financial instruments are unambiguously defined and can be evaluated under any economic assumptions;
  4. Security: There is no bug-free code. However, the most secure code is one that is used by many participants in many different environments. Open Source guarantees a wide audience and a maximum variation in the environments where the code is used and validated.

The logical conclusion that follows from these observations is that the cash flow obligations of financial contracts should be represented by algorithms rather than expressed in natural language.  In fact, at the operational level, this is what takes place.  The volume of business in banking is far too great to be handled with manual processes.  

For many years, financial Institutions have already relied on core-banking and transaction processing systems that embody the algorithmic representation of contractual obligations.  These systems turn the contractual obligations of natural language contracts into automatically computed payments with little or no manual intervention.  

However, the problem with the current core banking and transaction-processing systems is their unstandardized nature.  A single financial institution can have scores of transaction processing systems that each use different logic structures and programming languages. They also use different terminology for what turns out to be the same contract terms. Furthermore, as mentioned above, banks need a mathematical representation of their financial contracts to conduct all their required analytical functions in the areas of accounting, risk management and regulatory reporting.  However, these analytical systems stand apart from the transaction processing systems; since they cannot use the mathematical representations available from transaction processing, they do not achieve the best possible analytical insights, and they add significant and avoidable operating costs to the banks.

The ACTUS Organizations[edit | edit source]

Legal Structure[edit | edit source]

ACTUS has created two corporate structures to develop and promote the ACTUS financial contract standard.

  • The ACTUS Financial Research Foundation is a 501(c)(3) not-for-profit research institution[14].  As such, contributions to the Foundation are deductible from taxable income in the United States. Its charter provides for the creation and promotion of the ACTUS standard. To promote widespread adoption of the ACTUS standard the software implementation of the CTs is being made available without charge to all interested users under an open source license.
  • The ACTUS Users Association is a 501(c)(6) not-for-profit trade association[15].  Its charter is to be the primary vehicle for engagement with ACTUS users in all sectors – industry, academia, and regulatory agencies.  The contributions of users to this open source project will expand the resources devoted to completing the full set of ACTUS CTs and help insure that the official releases of the ACTUS software are fully tested and useable.

ACTUS Financial Research Foundation Board of Governors:[edit | edit source]

ACTUS has created two corporate structures to develop and promote the ACTUS financial contract standard.

    • Jefferson Braswell  (Chair)
    • Professor Wolfgang Breymann, Docteur de l’Université Paris 6 (Secretary)
    • Jan Klein (Treasurer)
    • Arie Y.Levy-Cohen
    • Robert Mark, Ph.D
    • The Honorable Allan I. Mendelowitz, Ph.D (President)

ACTUS Users Association Board of Governors:[edit | edit source]

    • Willi Brammertz, Dr.oec (Chair)
    • Jefferson Braswell
    • Professor Wolfgang Breymann, Docteur de l’Université Paris 6 (Secretary)
    • John A. Bottega
    • Jan Klein (Treasurer)
    • Arie Y. Levy-Cohen
    • Robert Mark, Ph.D
    • The Honorable Allan I. Mendelowitz, Ph.D (President)

Willi Brammertz, Dr.oec.: Founding partner of IRIS, now part of Wolters Kluwer, and father of one of the world’s leading financial analysis systems. Founder and President of Ariadne Business Analytics; Lead Author of the book “Unified Financial Analysis – the missing links of finance” that provides the basic concepts on which is ACTUS built

Jefferson Braswell: Founding Partner and CEO, Tahoe Blue Ltd; Member of the Board of Directors, Global LEI Foundation (GLEIF); formerly, President and CTO of Risk Management Technologies (RMT Berkeley, provider of the Radar Enterprise Risk Management Solution, acquired by FICO).

John A. Bottega; Executive Director, EDM Council; formerly Chief Data Officer of Citibank, Bank of America, and the Federal Reserve Bank of New York.

Professor Wolfgang Breymann, Dr.phys.:  Head of Research Area at Institute of Process Design and Data Analysis, School of Engineering, Zurich University of Applied Sciences, Co-Author of “Unified Financial Analysis” and with Mendelowitz and Brammertz part of the core team that launched the ACTUS project.

Jan Klein: CFO MCT Worldwide, formerly Professor of Finance, Equity Analyst and Venture Capitalist; CPA.

Arie Y. Levy-Cohen: Founder-CEO of Blockhaus Investment AG, co-Founder of SingularDTV, President of Integrated Engineering Blockchain Consortium (IEBC) and Chairman of CoEngineers.io. Former International Advisor and Private Banker with Morgan Stanley, where he also administered the Morgan Stanley Bitcoin Forum

Robert Mark, Ph.D: Managing Partner of Black Diamond Risk Enterprises, which provides corporate governance, risk management consulting, risk software tools and transaction services.  He is the Founding Executive Director of the MFE Program at the UCLA Anderson School of Management. Dr. Mark serves on several boards, was awarded the Financial Risk Manager of the Year by GARP and is a Cofounder of PRMIA. He was a Chief Risk Officer and led Treasury/Trading activities at Tier 1 banks as well as co-authored three books on Risk Management.

The Honorable Allan I. Mendelowitz, Ph.D:; Formerly Chairman of the Federal Housing Finance Board; Co-Leader of the Committee to Establish the National Institute of Finance; Executive Director, Congressional Trade Deficit Review Commission; Executive Vice President, U.S. Export-Import Bank; Managing Director U.S.GAO; Brookings Institution Economics Policy Fellow.

The ACTUS Future[edit | edit source]

ACTUS is an open-source project. In addition to the launch funding, much of the work developing ACTUS to this point has been contributed by volunteers who have committed their skills and personal funds to furthering the effort. Going forward, ACTUS looks to expand the membership in the community directly contributing to the development of ACTUS.  The ACTUS Users Association is intended to function as a dues paying organization to solicit participation in promoting and implementing the  ACTUS project.  

Licensing and Community[edit | edit source]

The potential benefits from adoption of the ACTUS standard are substantial.  They hold the promise of significant reductions in the cost of regulatory reporting and compliance, while at the same time enabling a significant improvement in the quality of regulatory oversight[16] of both institutional and systemic risk.  While individual banks can realize significant operating cost reductions[17] irrespective of what other institutions do, realizing the maximum possible benefits requires widespread adoption of the standard, so that data can be shared without manual adaptation.  For this reason the ACTUS standard is considered to be a public good.  Making it available under an open source license on a fee-free basis avoids possible impediments to widespread adoption.

There are only two primary restriction associated with the license.  The first is that only unaltered official releases of the ACTUS software can be said to conform to the ACTUS standard, unless the relevant variations have been approved.  This restriction is required to protect the integrity of ACTUS as a standard.  Second, the distribution of the software is prohibited to individuals or destinations identified in U.S. export control laws and regulations as ineligible to receive such software.

Publications[edit | edit source]

Articles and books[edit | edit source]

  • Breymann Wolfgang (2018) “ACTUS: An open-source framework for modern finance.” Research Features no. 123, 82-85, Feb. 2018. https://researchfeatures.com/wp-content/uploads/2018/02/Prof-Dr-Wolfgang-Breymann-Mathematics.pdf
  • Brammertz Willi, Mendelowitz Allan, Müller Luka (2018) Bank of the Future This article written with Allan Mendelowitz and Luka Müller was presented at the “2018 Cambridge Conference on Business & Economics at the Murray Edwards College, Cambridge University in Cambridge UK on July 3, 2018.
  • Sel Marc, Diedrich Henning, Demeester Sander, Stieber Harald (2018) How smart contracts can implement “report once” is https://zenodo.org/record/884497#.W6tcAfZ9iUn
  • Brammertz Willi, Mendelowitz Allan (2018) From digital currency to digital finance:  The case for a smart financial contract standard “The Journal of Risk Finance” volume 19, issue 1.
  • Kavassaliis Petros, Stieber Harald, Breymann Wolfgang, Saxton K., Gross Francis,  and Bertolo S., (2017) An innovative RegTech approach to financial risk monitoring and supervisory  reporting, {The Journal of Risk Finance} volume 19, issue 1, pages 39-55, https://www.emeraldinsight.com/doi/abs/10.1108/JRF-07-2017-0111 (2017).
  • Breymann Wolfgang, Bundi Nils Andrej, Johannes M., Stockinger Kurt (2017) “Large-Scale Data-Driven Financial Risk Assessment''. In: Braschler Martin; Stadelmann Thilo; Stockinger Kurt [eds.]: Data Science Applications. (Springer, Berlin, 2017).
  • Mendelowitz Allan, Brammertz Willi (2016) Smart Contracts, American Banker November 17, 2016:
  • Brammertz Willi, Mendelowitz Allan (2014) Limits and Opportunities of Big Data for Macro-Prudential Modeling of Financial Systemic Risk, Conference paper DOI: 10.1145/2630729.2630741 Improving Systemic Risk Monitoring and Financial Market Transparency- Standardizing the Representation of Financial Instruments: This internal paper was delivered at the OFR/FED (Federal Reserve Board Cleveland) conference in Washington on May 30, 2013 “Financial Stability Analysis: Using the Tools, Finding the Data”.
  • Brammertz Willi (2013) The Operational Risk of the Office of Financial Research (OFR), Published in: Lemieux, Victoria L, Ed. “Financial Analysis and Risk Management” Frankfurt: Springer 2013, Chapter 3.
  • Brammertz Willi, Mendelowitz Allan (2010) The Regulatory Revolution, Risk Professional (GARP) issue August 2010
  • Brammertz Willi (2010) Risk and Regulation, The Journal of Financial Regulation and Compliance, Vol. 18, No 1, 2010
  • Brammertz Willi, Akkizidis Ioannis, Breymann Wolfgang, Entin Rami, Rüstmann Marc (2009) Unified Financial Analysis – The Missing Link of Finance, John Wiley &Sons

Presentations[edit | edit source]

  • Bier, Werner. Deputy Director General, Statistics ECB (2018), “Standardization, a shift in the ecosystem of finance”. Speech held in London on February at “Chief Data&Analytics Officer”. https://www.slideshare.net/Chief_Data_Officer_Forum/chief-data-analytics-officer-uk-werner-bier
  • Bier, Werner. Deputy Director General, Statistics ECB (2018), “A glimpse into the future how might technology impact the needs and potential for measurement, both for businesses and for authorities?”. Speech held in Brussels on June at “Preparing supervisory reporting for the digital age”. https://ec.europa.eu/info/sites/info/files/finance-events-180604-presentation-bier_en.pdf
  • Braswell, Jefferson. (2016), “Operational Models for Effective Financial Risk Management and Regulatory Reporting”, paper presented at the Goethe University House of Finance, December, Frankfurt, available at http://www.efinancelab.de/fileadmin/documents/16-12-05-efl-safe/20161205_Financial_Contract_Algorithmic_Data_Standards_and_Operational_Models_Braswell.pdf
  • Breymann, Wolfgang., Mendelowitz, Allan. (2015), "ACTUS: A Data Standard That Enables Forward Looking Analysis for Financial Instruments?”, paper presented at the Joint Workshop Bank of England, European Central Bank and U.S. Office of Financial Research "Setting Global Standards for Granular Data”, January, London
  • Mendelowitz, Allan. (2018) “Regulation 2.0: The Right Data for Stress Tests and Oversight of Financial Risk”  3rd COST Conference, ZAHW, September 6, 2018 Winterthur, Switzerland
  • Brammertz, Willi and Mendelowitz, Allan. (2018) “ACTUS: A Data Standard for Deriving Analytic Content from Swaps Data”, Commodity Futures Trading Commission, April 30, 2018, Washington, DC.
  • Mendelowitz, Allan. (2017) “SMART CONTRACTS: The Key to Regulation 2.0 and More Efficient Banks”. Society for Economic Measurement Annual Conference, July 28, 2017, Cambridge, MA
  • Mendelowitz, Allan. (2017), “ACTUS: The Smart Contract Standard for Regulatory Oversight of Financial Risk”, FinTech Working Group, FDIC, January 11, 2017, Washington, DC
  • Mendelowitz, Allan. (2016), “ACTUS: The Data Standard That Enables Financial Analysis”, Financial Data Summit 2016, March 29, 2016, Washington, DC
  • Mendelowitz, Allan. (2015), “ACTUS: A Data Standard That Enables Forward-Looking Analysis for Financial Instruments”, Advancing Data Seminar, U.S.OFR, December 14, 2015, Washington, DC
  • Mendelowitz, Allan and Bundi, Nils. (2015), “ACTUS Use Case for Standardization: Stress Testing” Setting Global Standards for Granular Data: Joint Workshop of the Bank of England, The European Central Bank, and the U.S.OFR, October 29-30, 2015, New York, NY
  • Brammertz, Willi, Breymann, Wolfgang, and Mendelowitz, Allan. (2015), “ACTUS: A Data Standard That Enables Financial Analysis Based on Granular Transaction and Position Data”, Society for Economic Measurement Annual Conference, July 22, 2015, Paris
  • Brammertz, Willi and Mendelowitz, Allan. (2015), “ACTUS: Enabling Static and Synamic Analysis Based on Granular Financial Data”, Conference of the Conssortium for Systemic Risk Anaytics, May 27, 2015, Cambridge, MA
  • Mendelowitz, Allan. (2014), “A Financial Data Standard for the Analytic Use Case”, Conference of the Consortium for Systemic Risk Analytics, June 11, 2014, Cambridge, MA
  • Mendelowitz, Allan. (2014), “ACTUS: The New Standard for Financial Data”, SIFMA TECH 2014, June 18, 2014, New York, NY
  • Mendelowitz, Allan. (2013), “Improving Systemic risk Monitoring and Financial Market Transparency: Standardizing the Representation of Financial Insturments for Analysis”, The Open Financial Data Group, December 13, 2013, Washington, DC
  • Mendelowitz, Allan. (2013). “Improving Systemic Risk Monitoring and Financial Market Transparency: Standardizing the Representation of Financial Instruments for Analysis”, ISITC Industry Forum, September 9, 2013, Baltimore, MD
  • Mendelowitz, Allan, Brammertz, Willi, Breymann, Wolfgang, Khashanah, Khaldoun. (2013), “Improving Systemic Risk Monitoring and Financial Market Transparency: Standardizing the Representation of Financial Instruments for Analysis”’ European Central Bank, November 14, 2013, Frankfurt
  • Mendelowitz, Allan. (2013), “Improving Systemic Risk Monitoring and Financial Market Transparency: Standardizing the Representation of Financial Instruments for Analysis”, Global Association of Risk Professionals, October 13, 2013, Washington, DC
  • Mendelowitz, Allan, Brammertz, Wili, and Khashanah, Khaldoun. (2013), “Improving Systemic Risk Monitoring and Financial Market Transparency: Standardizing the Representation of Financial Instruments”,Joint Conference of the Federal Researve Bank of Cleveland and the U.S. OFR “Financial Stability Analysis: Using the Tools, Finding the Data, May 30, 2013, Washington, DC
“Cash flow” is the term of art used by ACTUS to refer to the payment obligations between counterparties to a financial contract.   A “cash flow” obligation may be paid in any accepted form of settlement, such as an electronic payment, a paper check, or in the likely rare case, paper currency.

References[edit | edit source]

  1. 1.0 1.1 "ACTUS". actusdraft. Retrieved 2018-11-04. 
  2. Unified financial analysis : the missing links of finance. Brammertz, Willi., Akkizidis, Ioannis S., Breymann, Wolfgang., Entin, Rami., Rustmann, Marco. Chichester, West Sussex, UK: Wiley. 2009. ISBN 9780470697153. OCLC 271772493. 
  3. "Unified Financial Analysis: The Missing Links of Finance". Wiley.com. Retrieved 2018-12-31. 
  4. "Better data needed to regulate the markets". Financial Times. Retrieved 2018-12-05. 
  5. Tracy, Ryan (2018-02-19). "Washington's $500 Million Financial-Storm Forecaster Is Foundering". Wall Street Journal. ISSN 0099-9660. Retrieved 2018-12-05. 
  6. 6.0 6.1 "ACTUS: An open-source framework for modern finance" (PDF). Research Features. 123: 82–85. February 2018. 
  7. "Stevens Institute of Technology: ACTUS - Algorithmic Contract Types Unified Standard". 
  8. Brammertz, W.; Akkizidis, I.; Breymann, W.; Entin, R.; Rüstmann, M., eds. (2012-01-02). "Unified Financial Analysis". doi:10.1002/9781119206071. 
  9. "ACTUS Contract Type taxonomy". 
  10. "Algorithmic Standard document". 
  11. Brammertz Willi, Mendelowitz Allan (January 2018). "From digital currency to digital finance:  The case for a smart financial contract standard". The Journal of Risk Finance. volume 19, issue 1. 
  12. Stockinger, Kurt. "Modeling capitalism in the 21st century". Science Node. Retrieved 2018-12-05. 
  13. Stockinger, Kurt. "Cash flow calculations using Monte Carlo simulations". primeurmagazine.com. Retrieved 2018-12-05. 
  14. "IRS". apps.irs.gov. Retrieved 2018-12-05. 
  15. "IRS". apps.irs.gov. Retrieved 2018-12-05. 
  16. Brammertz, Willi (2010). "Risk and Regulation". The Journal of Financial Regulation and Compliance. 18. 
  17. Kavassaliis, Petros et. al. (2017). "An innovative RegTech approach to financial risk monitoring and supervisory reporting". The Journal of Risk Finance. 19: 39–55. 


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