Fraud Prevention, Robo-Advisory Services, and Credit Scoring Transformed Through Machine Learning

ML speed and precision enable financial services to meet challenges related to efficiency and cost, finds Frost & Sullivan's Digital Transformation team

LONDON, Sept. 13, 2017 /PRNewswire/ -- Machine learning (ML), a branch of artificial intelligence (AI), is expected to become a standard in financial services in the next five years. As proofs of concept and use cases come to the fore, myriad applications of ML will impact several business functions. Fraud prevention, robo-advisory services, regulatory compliance and credit scoring will provide tremendous growth opportunities for the application of ML in financial services.

Frost & Sullivan's research, Disruption in Global Financial Services, 2017—Machine Learning is Imperative, provides an overview of ML market dynamics, including technology trends, drivers, and challenges for adoption. Case studies and profiles of some of the key players in the report cover Google, IBM, Orange, Swisscom, Onfido, Darktrace, Klarna, Infosys, SAP, and Rasa.ai.

To access more information on this analysis, please visit: https://goo.gl/4CtAwr

"The biggest advantage of ML solutions is their ability to learn from every transaction and instance. Today, companies and consumers are more comfortable with hybrid services. However, the fact that machines are evolving at a rapid pace, learning continuously and using this knowledge to improve customer satisfaction and experience is the biggest differentiator," stated Digital Transformation Senior Industry Analyst Deepali Sathe. "ML enables speed and precision, which are crucial inputs to financial services companies' abilities to meet challenges related to efficiency and costs."

Strategic imperatives for success and growth include:

• Different industry participants such as regulators, incumbents, and start-ups working together to create a strong eco-system that can fully utilise the potential of ML;
• Providing secure access to data to enable ML systems to identify normal and abnormal behaviours;
• Ease of use and security of data and transactions when using robo-advisory services;
• Ability to capture both structured and unstructured data to enable ML to master cognitive abilities and pick out behaviour that points toward fraudulent patterns; and
• Strong back-end algorithms to provide relevant outcomes for services such as credit scoring and financial inclusion.

"Paucity of people with knowledge and skill sets related to ML and lack of adequate training are limiting factors for rapid escalation of ML," noted Sathe. "On the other hand, market education is essential; financial companies are still not completely aware about ML, its benefits and impact on business outcomes. Combine this with associated costs and expenses in maintaining legacy infrastructure, and ML is still about three to four years from becoming mainstream."

Disruption in Global Financial Services, 2017—Machine Learning is Imperative is part of Frost & Sullivan's Digital Identification Growth Partnership Service program.

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