Machine Learning transforms fraud prevention
The precision of machine learning (ML) enables financial services providers to overcome efficiency and cost challenges, emphasizes Frost & Sullivan's Digital Transformation Team. Robotic services and credit scoring are expected to become standard for financial services providers.
Machine learning, as an area of artificial intelligence (AI), is expected to become the standard in financial services over the next five years. As proofs-of-concept and use cases come into focus, myriad uses of ML will impact different business functions.
Fraud prevention, robotic services, regulatory compliance, and credit scoring will create tremendous growth opportunities for the use of ML by financial services firms.
Case studies
The latest Frost & Sullivan study, "Disruption in Global Financial Services, 2017-Machine Learning is imperative," provides an overview of the market dynamics of machine learning and covers technology trends and drivers, as well as barriers to market adoption. It also provides case studies and profiles of some key market players, including Google, IBM, Orange, Swisscom, Onfido, Darktrace, Klarna, Infosys, SAP, and Rasa.ai.
"The biggest advantage of ML solutions is their ability to learn from every transaction and case. Today, businesses and consumers find it easier to deal with hybrid services. However, the fact that machines evolve very quickly, learn continuously and this knowledge can be used to improve customer satisfaction and experience - that is the biggest differentiator," explains Digital Transformation Senior Industry Analyst Deepali Sathe.
"ML enables speed and accuracy, and these are critical criteria for companies in the financial services sector that are facing growing challenges in terms of efficiency and cost."
Strategic needs for success and growth include:
- various industry participants, such as regulators, incumbents, and startups, working together to build a robust ecosystem where ML's potential can be fully realized;
- Provide secure access to data to help ML systems detect normal and misbehavior;
- Ease of use and security of data and transactions when using robotic services;
- The ability to capture both structured and unstructured data to enable ML to master cognitive skills and spot behaviors that reveal a fraud scheme; and
- strong back-end algorithms to offer relevant results for services such as credit scoring and financial inclusion.
"A lack of professionals with knowledge and skills related to ML and a lack of training opportunities are preventing ML from spreading quickly," Sathe said. "On the other hand, education in the market is essential. Financial firms are still not fully aware of the opportunities ML offers and what the associated benefits and implications are for their own business. If these aspects are combined with the associated costs and expenses, in terms of maintaining legacy infrastructure, then ML only needs three or four more years to become the standard in this industry."
Further free English-language information on this study can be found at here
The study, "Disruption in Global Financial Services, 2017-Machine Learning is imperative," is part of Frost & Sullivan's Digital Identification Growth Partnership Service program.
