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In 2023, the shortage of skilled data scientists is still a challenge for financial institutions. According to Indeed.com, the popular recruiting site, searching for “Data Scientist Financial Services” returns 1745 results. McKinsey & Company studied over a dozen banks in Europe that have replaced older statistical-modeling approaches with machine-learning techniques and saw significant improvements in their business metrics.

With the increasing importance of data analytics in banking, the shortage of skilled data scientists is becoming increasingly serious. Tools for collecting, sifting, and sorting data become faster, cheaper, and better, but people with the skills to make use of the results are harder and harder to find.

Cloud-based machine learning services can help fill the talent gap by opening up opportunities for junior or internal hires to augment risk analytics teams, provide immediate value, and grow into more advanced roles. Machine Learning can train and deploy a credit risk model in about 20 minutes, even by someone with little to no experience.

Machine learning is not just a temporary solution to a talent problem. McKinsey & Company’s study of European banks revealed increases in sales of new products, savings in capital expenditures, increases in cash collections, and declines in churn after replacing older statistical-modeling approaches with machine-learning techniques.

Combining machine learning with automated risk decisioning can prove invaluable to a financial institution’s bottom line. Automated risk decisioning helps make better credit decisions and improves overall portfolio performance.

Machine learning is the solution to the data scientist talent gap in the banking industry. Cloud-based machine learning services can provide immediate value and help junior or internal hires grow into more advanced roles. The benefits of machine learning are significant and can positively impact a financial institution’s bottom line.