Artificial Intelligence Can Rescue Government Agencies Stuck in COBOL Quagmire

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COBOL is a 61-year old computer language for processing data. Although highly inefficient by modern standards, millions of COBOL programs remain pervasive in government and industry and are responsible for transactions worth three trillion Dollars. Recreating them in contemporary computer languages is extremely time consuming, laborious, and expensive. Also, there is an acute shortage of COBOL programmers since universities no longer teach this language. Scry Analytics recently introduced Collatio - Data Flow Mapping software that "reverse engineers" COBOL programs and reduces the dependence on COBOL programmers by 85% and the cost of conversion by 75%.

“Artificial Intelligence (AI) can rescue federal and state governments who are struggling to upgrade outdated COBOL software programs, thereby helping them accelerate the disbursal of Covid-19 related funds and process unemployment claims promptly.”

“Artificial Intelligence (AI) can rescue federal and state governments who are struggling to upgrade outdated COBOL software programs, thereby helping them accelerate the disbursal of Covid-19 related funds and process unemployment claims promptly,” states Dr. Alok Aggarwal, CEO & Chief Data Scientist, Scry Analytics.

COBOL is a 61-year old computer language for processing data and is unable to scale up and handle numerous requests quickly. This inadequacy has become a massive bottleneck during the COVID-19 pandemic as illustrated by the examples given below:

  • The US Internal Revenue Service scrambled to patch its COBOL-based Individual Master File in order to disburse around 150 million payments mandated by the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
  • With the ensuing unemployment surge in New Jersey, Governor Phil Murphy recently put out a call for volunteers who know how to code in COBOL, because many of New Jersey's systems still run on old mainframes. The state of Connecticut also admitted that it was also struggling to process the large volume of unemployment claims with its 40-year-old COBOL mainframe system.

Since the cost of replacing COBOL code is around 25 Dollars per line, the total cost and time of replacing 200 billion lines of code in the US and elsewhere will be about five trillion Dollars and 40 million person years, wherein approximately half (2.5 trillion Dollars and 20 million person years) will have to be spent in deciphering COBOL programs. On the other hand, COBOL experts who can decipher these programs are in short supply with only two million such programmers remaining in the world of which half are retired. Moreover, colleges no longer teach this language, and the few graduating students who know COBOL do not want to use it for the fear of being labelled as ‘blue-collar tech workers.’

Although there are software tools that convert a given COBOL program into another computer language, these tools are essentially “black boxes” whose accuracy is often low. In contrast, Scry Analytics has developed software, Collatio – Data Flow Mapping, that uses proprietary AI-enabled algorithms and helps experts in swiftly inferring steps executed by the COBOL program, thereby reducing their time by 85% and cost by 75%. Since the input and output tables related to the COBOL program contain a “fingerprint” of various steps executed by the program, rather than scrutinizing it, Collatio – Data Flow Mapping determines the relations among these tables, and helps the expert in inferring various steps and creating the flow-chart.

Company Details – Scry Analytics
Scry Analytics (http://www.scryanalytics.ai) was founded in 2014 and builds innovative AI-based enterprise applications that enable clients to rethink and automate their data-driven and manually intensive business operations. Scry’s family of apps include Collatio (for ingesting, extracting, and reconciling unstructured and structured data), Anomalia (for detecting anomalies and potential fraud), and Risc (for predicting operating and marketing risks).

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Alok Aggarwal
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