The Risks and Limitations of Current Information Management Practices in Financial Crimes Compliance Initiatives Background
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The Risks and Limitations of Current Information Management Practices in Financial Crimes Compliance Initiatives

October 20, 2017
· 2 min read

In my earlier blog post, I discussed some of the findings from a recent FIMA benchmark study regarding the current state of information management for financial institutions (FIs). The statistics showed that FIs continue to be burdened by compliance-related issues and remain highly dependent on IT departments to address them.

Today I’d like to talk about the shortcomings of the current approaches to financial crimes compliance.

Compliance reporting is a time-sensitive process, and it is vitally important for FIs to meet deadlines. Speed is of the essence, but unfortunately, the current practices are not fast enough for the vast majority (78%) of FIs. In fact, one in three responded that most of their projects fail because oftentimes the data received by the lines of business (LoB) from IT departments comes too late.

Not only are FIs disgruntled with the lack of speed in existing, Excel-based data management practices, they are also frustrated by its lack of accuracy:

  • Only 5% are fully confident in the results
  • 43% say that data quality and governance are significant challenges for them
  • Only a mere 2% are “thrilled” with the quality of the data output they receive
  • Just 12% rate the quality of their data as “very good”

This shows that the current approaches to compliance reporting – where IT and LoB operate in silos and “collaborate” via Excel – is deficient in many respects.

Considering that data accuracy and quality are tightly associated with risk management, protecting the brand and minimizing fines, FIs clearly need a better approach and a fresh look at new technologies.

In the next blog post, we will discuss how new disruptions in technology solves the current issues arising from the lack of speed and trust in data as discussed above.

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Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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