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How New Technologies are Helping Banks Solve the Big Data Challenge

By Ryohei Fujimaki

Data science is a major area of investment for banks due to its proven impact on cybersecurity and fraud protection, risk mitigation, customer relationship management and more. When fully operationalized in production, data science enables banks to make data-driven decisions with unprecedented levels of speed, transparency and accountability, accelerating  digital transformation initiatives and delivering better financial products and services that meet customers’ needs. Time-to-market to delivery data science impact is crucial to success, especially for traditional retail banks with physical branches and high overhead who must find innovative ways to compete with their online counterparts.

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Speed Bumps on the Road to Automation

Financial institutions face many roadblocks, both external and internal, when it comes to automation adoption according to a recent Cognizant study. From security issues (91 percent of respondents identified this as a “high” or “medium” challenge) and development (91 percent), to servcie production line cooperation (84 percent) and ongoing maintenance (89 percent), it is no wonder that the study has found that 65 percent of automation efforts remain in the proof-of-concept stage.

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AI Enables More Efficient Access and Storage of Files

Plano, Texas // www.m-files.com

Recently, Iceland-based bank Kvika selected M-Files, an intelligent information management solution, to help the firm effectively organize and manage documents in its branch offices, as well as its loan and pension savings departments. The solution gives the bank the ability to easily integrate with a custom-built customer relationships management system and accelerates loan processing between Kvika’s front office, loan department and back office.

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Creating a More Efficient Lending Process with Loan Pathing

By Neill LeCorgne

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The Banking Marketing Conflict: Personalization vs. the Quarterly Lifecycle

By Tyson Nargassans

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