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10 data science considerations for financial institutions

Advanced data science promises numerous benefits to financial institutions in a wide variety of areas, including marketing, sales, operations, consumer intelligence, portfolio management, risk and compliance. Here are 10 significant areas of concern that financial institutions should consider as they evaluate their technology priorities:

  1. Artificial intelligence (AI) – AI can enable faster, more consistent decision-making. Perhaps the most promising form of AI is machine learning, which uses self-adaptive algorithms to identify patterns in data and make predictions about the probability of certain actions.
  2. Data warehouses and data lakes – although critics contend that centralized data warehouses are an outdated concept, they are critical for most financial institutions. The move to so-called data lakes has only served to create data swamps in which there are numerous islands of uncontrolled data.
  3. Putting consumers at the center – today’s most successful financial institutions place a high priority on consumer-centric initiatives that generate a 360-degree view of consumers, including interactions with the financial institution itself as well as their online presence.
  4. Cloud use – the use of cloud technology continues to expand, with more and more organizations using cloud servers rather than local hardware to host both data storage and business processes.
  5. Blockchain – the adoption of such distributed ledger tools is still in the early stages within the financial sector, but blockchain offers great promise in areas such as fraud reduction, improved know-your-customer and customer due diligence processes and the use of smart contracts for handling payments and other transactions.
  6. Cybersecurity – cybersecurity is a fast-changing area of concern, with a continuing need to develop solutions that anticipate what cybercriminals might attempt as they look for the path of least resistance to sensitive information.
  7. Data privacy regulations – regulations governing how financial institutions share and protect consumers’ private information are evolving, meaning that financial institutions need to continually review and evaluate their data privacy policies and practices.
  8. Dark data – financial institutions often retain astounding amounts of data that goes unused, unmined and/or is ungoverned. This so-called dark data could present an additional opportunity for useful intelligence; it also increases data exposure risks.
  9. Auditing – a central question growing out of many new technological advances is how audit committees and internal departments should adapt their policies and procedures in order to continue to fulfill their oversight responsibilities.
  10. Data governance – as technology advances and regulatory and financial reporting requirements continue to evolve, the risks associated with untrusted, ungoverned and uncontrolled data will continue to increase. The need for a trusted, single source of data with strong governance and control has never been greater, especially when data is being used for purposes for which it was not originally intended.

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