Machine learning is one of the most promising technologies today that makes it possible for machines to learn how the human brain works and replicate this learning to analyze varied data types and deduce meaningful insights.
Machine learning models
At the core of machine learning are three models that help machines unearth insights and patterns. These are:
- Supervised models: These are used with historical data where the output is pre-defined. For instance, when you speak, Alexa can recognize the words and sentences she has been trained on and respond appropriately.
- Unsupervised models: These are used on transactional data to identify patterns. Based on your interaction with Alexa, she can identify the patterns to suggest topics you may be interested in.
- Reinforcement learning: It is a technique where machines learn to respond to situations on their own, without instructions. For every mistake (a negative outcome) that Alexa makes, she ‘learns’ from it to become smarter and refine the response next time.
FIs can benefit the most from machine learning
Businesses are increasingly leaning on machine learning, as volumes of data are exploding and they need actionable insights to fuel business growth. Given the benefits it promises, numerous industries—manufacturing, energy, healthcare, cyber defense, financial institutions—are making significant investments in machine learning. In fact, financial institutions (FIs) stand to benefit the most from machine learning, according to a PwC report.
Money-rich FIs, especially banks, have always been a favorite target for criminals. And, today’s technological advancements have provided cyber criminals with sophisticated techniques—data breach, phishing, malware, sweatshops, and so forth—to break into business systems and cause losses.
Machine learning, with its innate ability to monitor millions of online transactions in real-time, can help financial institutions in a myriad of ways.
- Document interpretation: Machine learning helps financial institutions interpret financial and legal documents—bank statements, tax statements, contracts, etc—across a wide range of parameters that help gain in-depth insights into customers’ financial health.
- Risk management: Financial institutions can accurately assess the credit-worthiness of a customer—whether an individual or a company—and make informed lending decisions for improved risk management.
- Additional revenue: Using analytics to understand customer preferences and inclination to spend, financial institutions can harness these insights to pitch other products and services to increase their revenue.
- Customer service: Applying behavioral analytics, banks and financial institutions can better understand the financial needs of their customers and offer more relevant services. This enables financial institutions to strengthen customer relationships and earn their trust.
- Channel-agnostic access: Leveraging customer data to anticipate customers’ channel preferences, financial institutions can provide seamless user experience to their customers across devices and locations.
- Process automation: Machine learning helps financial institutions make automated decisions in real-time that reduces the response time. According to Accenture, FIs can reduce costs incurred on middle and back offices across infrastructure, maintenance, and operations by 20-25%.
- Security: With fraud on the rise, financial institutions are obliged to ensure online security of their customers. Customer security is the most important area where machine learning has proved immensely helpful in fighting fraud by accurately identifying fraudsters from a group of authentic customers. Real-time analysis of digital intelligence enables financial institutions to prevent fraud from poisoning their business ecosystem, thereby providing customers with a safe and secure online journey.
The article has been written Neetu Katyal, Content and Marketing Consultant
She can be reached on LinkedIn.