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.
However, in spite of this investment, big data is still causing big challenges, especially for regional and community banks and credit unions. These institutions are collecting unprecedented volumes of customer data from an increasingly complex network of sources; some sources predict that the amount of stored data will reach 44 zettabytes by 2020. This new data is both structured and unstructured, and the legacy data systems that banks invested in years ago are unable to handle the volume and complexity of data now coming in.
Innovations in AI and data science are changing that. But for many small and mid-sized banks, challenges in resources, technology infrastructure, and the ability to operationalize models quickly and efficiently can prevent financial institutions from fully leveraging the new technology to drive business impact. At the same time, banks and other financial services firms still struggle to attract and retain top technology and data science talent.
Which brings us to automation — the next step for banks who need to harness the power of big data while overcoming the tech and talent challenges that have held them back so far.
Automated data science platforms are scalable and customizable and can meet current data needs, accepting data from different sources and in different formats, so banks can more quickly analyze the data for model generation. These platforms also offer white box models, in which the influencing variables as well as the mathematical mechanism to analyze those variables are visible and clearly explained, enabling banks to make data-driven decisions with the transparency and accountability and create actionable, accurate models that meet regulatory requirements.
Automated data science platforms also help banks overcome perceived talent limitations. Traditional data science is an interdisciplinary domain that involves many resources from different functional areas. Often, these are resources that a community bank or credit union does not have available. Modern, automated data science platforms can alleviate this strain on resources, as they automate the entire data science process, from data collection through production-ready models, including feature engineering. This automation democratizes the process, enabling more participants with different skill levels to effectively execute on projects. This can accelerate the data science process from months to days without the need for additional data science talents.
Financial institutions can leverage automation to overcome the biggest challenges to driving business impact from their data science investments. End-to-end data science automation makes it possible to execute data science processes faster, often in days instead of months, with more transparency. As a result, regional and community banks and credit unions can rapidly scale their AI/ML initiatives to drive transformative business impacts.
Ryohei Fujimaki, Ph.D., is founder and CEO of dotData.