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Digital technologies such as cloud, advanced analytics, artificial intelligence and big data are reshaping the financial services industry. Cutting-edge financial institutions are using these technologies to better meet the demands of clients and regulators while working to bolster the bottom line. In this post, you'll learn about the state of digitization in the financial services industry and the various ways in which financial organizations are approaching it. And over the course of our series on data and the financial services sector, we'll cover:
In an environment where data and data analysis is everything, and countless man hours are spent analyzing information, financial services firms need a more efficient solution to answering questions about loan security, risk assessment and customer satisfaction.
While the value of data and its relationship to success is well understood in financial services, most new and increasingly valuable data is unstructured or not captured within a firm (dark data). The reason is straightforward: Legacy systems cannot support unstructured data without adding significant IT complexity.
With money on the line, some financial experts are bringing big data to the financial services sectors to transform the landscape to be more forward thinking. For example, American Express uses machine learning to benefit its card members and business by processing petabytes of information to better mitigate risk, personalize offers and drive efficiency.
The company has been on a multi-year mission to incorporate a big data infrastructure with machine learning directly into its business after realizing that traditional databases could no longer handle the amount of data and breadth of analytics needed to improve services and manage risk. Machine learning applications need to work with large amounts of data from a wide range of sources. Therefore, the move to an enterprise-wide platform is a top priority for firms that continue to integrate big data into everyday operations for more efficient processes.
Where does the financial services industry stand in terms of digital maturity? Digital maturity varies among financial firms of all sizes and segments. Morgan Stanley's Digitalization Index shows how far down the financial sector is in comparison to other industries:
While the financial industry is lagging behind, it won't be for long: the majority of CFOs in all industries worldwide the majority of CFOs in all industries worldwide (58 percent) expect the amount of data they process to increase by up to 50 percent by 2020.
When it comes to dealing with big data, organizations must think big in order to see big results.
Many financial services firms focus their technology on solving current problems. However, as the financial services sector shifts rapidly toward digitization, long-term goals must be taken into account as well. Large financial firms are trying to transition from small discovery use cases to managing new volumes of data for solving problems, unlocking inefficiencies and creating new value-added services. For example, TransUnion launched a new self-service analytics platform to help customers assess their risk strategies, while American Express uses machine learning techniques across a wide range of interactions to personalize customer offers and streamline point of sale decisions. To move from small discovery use cases to using data enterprise-wide, financial services firms need to adopt a comprehensive approach to digitizing their organizations. McKinsey & Company identified five key approaches that organizations adopt in sequence to achieve widespread results:
Are financial services firms ready to make that transition? That's what we'll explore in our upcoming series of posts on the state of big data in the financial services industry. For more, check out our latest ebook, "MapR Industry Guide to Big Data in Financial Services." And stay tuned for our next post on how financial services companies are winning with data.
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