How machine learning is impacting the finance industry
Machine learning streamlines and optimizes processes ranging from credit decisions to quantitative trading and financial risk management.
This exciting technology has the potential to transform business models and financial services markets for blockchain-based commerce, credit and finance, reduce friction, and improve product offerings.
Machine learning is a subset of artificial intelligence that uses advanced statistical techniques to enable computer systems to improvisatione to tasks with experience over time. Chatbots like Amazon’s Alexa and Apple’s Siri are improving every year thanks to constant consumer use coupled with machine learning that takes place in the background.
Machine learning has grown considerably within the financial industry, enabled by the abundance of data available and the increasing affordability of computing capacity.
The technology is increasingly deployed by financial services organizations in the following areas:
Machine learning in finance has a huge impact; Let’s see how.
1. Personalized products and services
Gone are the days when financial services only meant saving money in the bank or taking out a loan. Machine learning expands the range of financial services through so-called consumer financial services. Consumer Financial Services puts consumers and their unique demands at the heart of their highly optimized offerings. Machine learning helps provide consumers with a personal financial concierge that allows you to automatically decide on an appropriate spending, saving and investing style based on your personal habits and goals. With machine learning in finance, it’s possible to create smart products that can learn from your financial data and determine what works for you and what doesn’t, and help you better track your financial activities.
2. Reduced transaction cost
This is something we all must have been through and therefore would be okay with. Machine learning in finance has automated processes and dramatically reduced the cost of serving clients. While machine learning has, on the one hand, reduced the cost of financial services, on the other hand, it has made financing extremely convenient to use. Through various digital service channels, machine learning is proving effective in attracting this large segment of the population to financial services, which previously found them cumbersome, expensive and time consuming.
3. New management styles
Machine learning in finance opens new avenues for banking and insurance executives to seek advice. Financial experts are no longer limited to human opinions to make forecasts or recommendations in the field of finance. With machine learning in finance, these executives can now ask the machines questions that are relevant to their business, and those machines can, in turn, analyze the data and help them make data-driven management decisions. As far as consumers are concerned, they can have their financial portfolio managed with virtually no management fees and with great efficiency, instead of using the services of a traditional advisor who can charge around 1% of your investments.
4. Anticipatory fraud scenarios
With machine learning, it is possible to simulate countless situations where fraud or cybercrime can occur. Machine learning in finance therefore takes a proactive approach to making the financial services environment secure and tamper-proof. Unlike before, the designers of a financial services system do not have to wait until a fraud incident is detected before securing a system. Machine learning helps the finance industry to innovate freely by securing its products and services through an ongoing understanding of human psychology. In addition, machine learning in finance also helps maintain strict regulatory oversight. Machine learning ensures that all policies, regulations and security measures are strictly adhered to when designing and delivering any financial service.
5. Automation of transactions
Critical decisions in fields like finance cannot afford to be marred by the vagueness involved in human decisions. Machine learning in finance involves deep research, understanding, and learning over long periods of time and large volumes of data. Machine learning introduces automation in areas that require a high degree of incisor, thus preserving consumer confidence.
Machine learning is all about continuous learning and relearning of models, data and developments in the financial world.
It gives financial organizations more flexibility build on their current systems, products and services.
Bank-related success chatbot interactions will increase by 3,1505% between 2019-2023.
826 million hours will be saved by banks thanks to chatbot interactions in 2023.
79% of successful chatbot interactions will be through mobile banking apps in 2023.