How Banks & Regulators are Applying Machine Learning

Annual worldwide AI revenue is projected to grow from $644 million in 2016 to $37 billion by 2025, with top use cases including algorithmic trading strategy performance improvement; static image recognition, classification, and tagging; efficient, scalable processing of patient data; predictive maintenance; content distribution on social media; and more.

The financial services industry is no stranger to machine learning – a number of large institutions continue to successfully implement the technology across such areas as risk analytics and regulation, customer segmentation, cross-selling and upselling, sales and marketing campaign management, creditworthiness evaluation. Among institutions that are applying machine learning are BBVA, JPMorgan Chase, HSBC, OCBC, and many more.

Credit applications and underwriting are the key areas where machine learning, and data analytics in general, will have an initial impact. The outcomes will include cost reductions, increased efficiency, and less onerous customer experiences, experts suggest.

McKinsey reports that in Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques and, in some cases, experienced 10% increases in sales of new products, 20% savings in capital expenditures, 20% increases in cash collections, and 20 percent declines in churn. The banks have achieved these gains by devising new recommendation engines for clients in retailing and in small and medium-sized companies. They have also built micro-targeted models that more accurately forecast who will cancel service or default on their loans, and how best to intervene, the consultancy says.

Let’s explore some interesting examples of machine learning applications in banking.

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