The financial services industry has always been at a rocket speed when it comes to adopting new technologies. The use of machine - learning has gained momentum throughout the financial services industry, although issues such as data quality continue to undermine its progress. Refinitiv, the financial data provider, said in a report that more than 90 percent of the organizations it surveyed either deployed machine learning in multiple areas of the organization or started in some pockets. Machine-learning relates to the use in financial markets of algorithms and statistical models without the use of human directions and depends instead on patterns to make choices.

Today, banks are facing substantial marginal pressure. Regulators have made several businesses unprofitable and their relentless focus on transparency. While financial services firms are still looking for new customers and new products, managing their bottom lines is absolutely imperative. It's here that AI comes in. AI technologies enable them to make their operations and cost management more efficient. This is further fuelled by the sharp rise in technologies such as Robotic Process Automation (RPA) and Intelligent Process Automation (IPA).

Financial organizations are increasingly embracing a learning-based approach to machine learning to increase their approach to surveillance and risk management based on algorithmic rules. Machine learning techniques that are learning all the time at work can be a few steps ahead of fraud detection systems based on human and rules. In detecting insider trading to market abuse, AI and machine learning prove to be game-changers.

With the aid of automated and chatbot-driven personalized interactions, several banks already use AI to drive customer centricity. The core idea behind customer centricity is hyper-personalization. The ultimate holy grail is to create (or customize) products for each customer. No organization is "perfectly" set-up in this modern era of financial services–the speed of change and its transformative nature make it impossible. The key is to be versatile and adaptive enough to enable technology such as machine learning to take advantage of the enormous benefit on offer.

Furthermore, in the foreseeable future, the daunting quest for quality data sources to be used in machine learning is likely to continue. While searching for new data assets is important, this can sometimes be like searching for a needle in the haystack.

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