How is Machine learning enhancing the finance sector?
The ability of computer programs to learn on their own and improve over time is creating new opportunities for industries across the world. Machine learning in the finance sector offers a new level of service for financial forecasting, customer service, and data security.
This useful trend of utilizing machine learning and applying the powers of predictive analysis in banking and the finance sector is gaining more popularity due to the availability of vast amounts of data and efficient computing power.
How has machine learning and predictive analytics found the necessary spot in the finance industry?
The major reasons responsible are-
- A high volume of data generated: This sector includes industries like- banks, investment companies, insurance companies, and real-estate firms. These industries collectively produce ocean amounts of data. This data is mainly financial and historical. Since machine learning has the power to digest given data sets and the ability to learn from that data, providing the ability to carry out specific tasks.
- Suitable and appropriate applications: Machine learning has produced many useful and important applications for the financial sector.
Let’s talk about some:
- Fraud prevention: For every $1 lost to fraud, financial institutions pay $2.92 in recovery and associated cost. Thus they bear a great responsibility to protect money from fraudulent activities. With the use of machine learning and predictive analysis, patterns are detected to block fraudulent transactions.
- Customer service: Machine learning and NLP trained chatbots to provide enhanced customer services. Providing personalization, smooth virtual experience, feedback and reviews, better match of product and customers, etc. With the end of 2020 chatbots will be handling more than 85% of customer interactions. According to Juniper report chatbots would save over $8 billion annually by 2022.
- Risk management: By using predictive analysis to huge amounts of data in real-time rouge investments can be identified. Providing better risk management, machine learning aided softwares are far ahead than the traditional software applications which were based on static financial information.
- Investment prediction: With the application of machine learning it has become easy and reliable for investors to identify the changes in the market trends. The algorithmic applications of machine learning automate the selling of the stock when the price drops down from the certain limit and vice-versa. Machine learning thus is paving way for the future of investment banking.
Along with the above-mentioned solutions, machine learning is playing an integral role in many phases of the financial ecosystem, from approving loans and carrying out credit scores, to managing assets and assessing risks. The major use cases of Machine learning in finance are:
- Liquidity planning
- Demand planning
- Credit scoring
- Process automation
- Digital assistants
- Algorithmic trading and many others.
There are several finance sector companies and banks using machine learning
- JPMorgan: Leverages NLP with its Contract Intelligence platform.
- BNY Mellon: Utilizes process automation in their baking ecosystem.
- Wells Fargo: Uses AI-driven chatbot
- Privatbank: Implements Chatbot assistant on all its platforms
- Zestfinance: uses an AI-powered underwriting solution that helps companies assess borrowers with little to no credit information or history.
Future aspects of Machine learning in Finance:
Despite the big players of the financial ecosystem being the early adopters, there is a need for other financial institutions to penetrate this market. If you dig deeper into this aspect you can see a lot is happening out there. With more awareness and encouraging even small organizations to implement this, we will be able to apply the use cases like:
- Efficient customer service
- Wealth management
- Consumer security
- Predict financial crises
- Keeping a check on illegal trading
- Sentiment analysis
Machine learning and artificial intelligence are set to transform the banking industry. Vast amounts of data is used to build models in machine learning that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate a value of more than $250 billion in the banking industry.
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