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AI can bring needed speed to respond to multiple challenges

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Artificial intelligence has been assuming a growing role in financial services in recent years, affecting areas such as credit decisions, risk management, fraud detection and stress testing.

According to a World Economic Forum report issued in January, 77% of finance executives expect AI and related technologies to have a high or very high importance to their businesses within two years.

Economic fallout from the coronavirus crisis, however, has moved up the timetable for financial services operations to become mass adopters of AI and harness its predictive powers sooner rather than later.

Financial institutions across the world are dealing with the effects of the COVID-19 crisis and face an uphill challenge in containing the impact on systems and the broader economy.

With rising unemployment and stagnated economies, individuals and companies are struggling with debt and the world is awash in credit risk. This has pushed operational resilience to the top of the agenda for banks' chief experience officers, requiring them to focus on the credit risk environment while continuing to deliver innovative digital services to customers.

To make matters worse, criminals are exploiting vulnerabilities imposed by the shift to remote operations post-COVID-19, increasing the risk of fraud and cybercrime. For financial institutions, building and maintaining robust defenses has become an even more critical priority. Banks across the globe are forging new models to combat financial crime in collaboration with governments, regulators, and other global banks.

All of this has made technology advances in streaming data analytics and artificial intelligence/machine learning (AI/ML) an essential driver in financial services firms’ response to the crisis and is accelerating the automation journey they had already embarked on.

Until recently, financial institutions have used traditional methods of data analysis for various applications, such as detecting fraud and predicting defaults, that require complex and time-consuming investigations. By enabling high-frequency analytics on large volumes of data sets and using AI/ ML, firms will significantly increase the speed and accuracy of analysis.

The financial services industry can capitalize on huge volumes of data sets they hold from diverse data sources across business units to train ML algorithms that can automate many of their processes and AI for operational resilience. The technology and tools for getting these capabilities into production keep getting better and more accessible to users beyond data scientists and AI experts, enabling financial institutions to speed up their AI/ML adoption.

During this time of economic disruption that has deep implications for the corporate sector, financial institutions need to make AI a core part of their effort to adjust to the new normal and take advantage of the enabling technologies that can propel adoption quickly.

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