As it has in numerous industries, data and technology are forcing change in insurance and financial services. And, artificial intelligence, machine learning and other emerging technologies are accelerating that change. In this roundup, we learn more about how these technologies – and others – have helped “unlock vast stores of data” for life insurance companies and have dramatically reduced time-to-issue in underwriting. We also learn about the challenges facing financial services companies in implementing successful data maturation plans. Lastly, we hear from Pitney Bowes about how data is key to helping financial institutions combat money laundering and other financial crimes.
Building the Life Insurance Policy of the Future: How Data, AI, and Cloud are Changing an Industry
Industry experts say that the life insurance underwriting business is about to change, thanks to a combination of cloud-based technologies, artificial intelligence (AI) and innovative insurance tech that unlocks vast stores of data from files, such as PDFs. With multiple data streams available, and tools like predictive analytics, data mining, prescription profiles, and even mortality-predicting facial recognition technology, the pace of change and innovation is speeding up. These new technologies are reducing the time it traditionally takes to issue policies from months or weeks to days and even hours. They also are reducing the bias that inevitably comes in writing and adjudicating a claim. To get started, life insurance carriers should focus on breaking down silos of information and unlocking the value of the data they already hold in legacy applications and file formats.
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3 Challenges for Chief Data Officers in Finance
During MIT’s Chief Data Officer and Information Quality Symposium, attendees discussed the top three challenges data officers face as they try to mature the data function in their financial services organizations. These include establishing a data office and setting parameters around what the CDO oversees – Is the CDO in charge of data science? Analytics? Privacy? Security? Is it a dotted-line responsibility to all of the above? The second is aligning the office with business objectives while still while maintaining backroom data quality and governance issues. This will require partnering with stakeholders around the organization to grow the business, deepen customer engagement, and identify new product and service opportunities. Another challenge is scaling data successes and ensuring that they create lasting value by producing data that can be used and reused endlessly, with value increasing each time.
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ACAMS 2019 Wrap-Up: Addressing Data Quality Challenges Becomes a Priority for Financial Services Institutions
At this year’s anti-money laundering and financial crime conference, hosted by the Association of Certified Anti-Money Laundering Specialists, the biggest trend was the near-universal consensus that data quality is the key to beating financial crimes and helping financial institutions become compliant. Among myriad fintech solutions, the discussion around data quality included the fact that new regulations in New York and Canada have helped the industry “turn a corner in a decades-long conversation,” according to Pitney Bowes. The focus on data quality was apparent in the number of fintech solutions that are powered by machine learning (ML) and artificial intelligence (AI) and the need for “good quality data to feed these tools.” Financial services organizations need to be able to quickly discover and profile specific types of data and apply specific treatments to the data.
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