Use Of Machine Learning In Finance

24 Nov Use Of Machine Learning In Finance

Machine learning allows a computer algorithm with past experience or historical data to work on an immense amount of data. This helps in data analysis and getting data-driven decisions. For example, Weather forecasting, medical diagnosis, image processing, etc.

The finance industry including, fintech firms, banks, and trading now use machine learning to automate time-consuming, daily tasks.

Let us discuss the chief usages of ML in the finance sector.

PROCESS AUTOMATION

Machine learning lets finance companies entirely replace manual routine work by automating repetitive tasks. For instance, Paperwork automation, Chatbots, and employee training gamification.

In this way, finance companies can save huge costs, improve customer experience, and enhance their services.

FINANCIAL MONITORING

Data scientists work on algorithms to provide enhanced cybersecurity solutions. The trained systems can detect any fraud or money laundering technique easily, which would have been possibly missed by human eyes.

Thereby, finance organizations use ML to financially monitor millions of data. Enhanced financial monitoring ensures securing transactions, getting real-time approvals, etc. These models are built on the basis of client behavior as per the internet and transaction history.

Credit card fraud detection is one of the most successful Machine learning applications.

MANAGING RISK

Banks and financial institutions use machine learning techniques to significantly lower the risk level. Traditional methods are only limited to important information such as credit scores, etc. But ML can analyze volumes of personal information to access and reduce risk.

For instance, various ML programs tap into data sources of customers applying for loans and allot risk scores to them.

ALGORITHM TRADING

Trading companies take the help of algorithm trading to monitor the trade results and news in real-time. it studies patterns of the fluctuating graph of stock prices. Thus, one can make informed trading decisions.

It assists in the following:

-Reducing human errors

-Increased accuracy with the least chance of mistakes

-Executing trades at the best possible prices

-Simultaneous checking of multiple market conditions

MANAGING CUSTOMER DATA

It is very challenging to process structurally diverse data from social media, transactional details, mobile communications, and many more. Machine learning brings process efficiency and extracts real intelligence from a massive chunk of data.

Data analytics, natural language processing, and data mining, help in getting valuable insights from data for better business decisions. 

IMPROVING CUSTOMER SERVICE

Business is influenced by customer behavior and choices. It is really important to engage customers and providing them instant solutions with direct interaction. Not only it helps in retaining clients but also in understanding their changing concerns and needs.

Specialized financial chatbots hold a significant example in the industry. One can get finance-specific interaction in abundance. Also, one can be assisted with a personalized investment offer.

LOAN/INSURANCE UNDERWRITING

ML can do the same credit-scoring and underwriting tasks with predictive algorithms that earlier consumed thousands of human hours. Banks and Insurance companies use machine algorithms to perform automated tasks. This includes looking for exceptions, determining the applicant’s eligibility for loan or insurance, and matching data records.

There are a number of companies that excel in Machine learning Loan/Insurance underwriting. For instance, the Los Angeles based ZestFinance company helps other finance companies to assess loan applicants who have little or no credit records.

TRADE SETTLEMENT

Trade settlement is the procedure of moving cash into the seller’s account and securities into the buyer’s account following trading stocks. Numerous trades are being settled automatically without any human interaction. Still, 30% of trades fail and need manual intervention to be settled.

ML can be very effective here. As it not only recognizes the cause of trade failure but can also analyze why the trades were rejected, give solutions, and also forecast risky trades in the future. It is a full package of solutions.

For instance, BNY Mellon performs research on the failed trades with robotic process automation.

CUSTOMER RETENTION PROGRAM

ML technology is used by credit card companies to recognize risky customers and retain selected ones out of these. Offers can be designed specifically for the users as per transaction activity and demographic data. Also, user behavior can also be predicted.

A predictive, binary classification model is used here to find out customers at risk. Further, a recommender model is used to ascertain best-suited card offers that can retain clients.

FINANCIAL ADVISORY

There are many budget management apps running in the market powered by machine learning. They track customer spending on daily basis and analyze the data. In this way, they determine the spending pattern and also where the money can be saved.

Robo-advisors are a rapidly emerging trend in this context. Customers with limited resources are targeted and their funds are managed. Full-fledged financial advice is given to users such as retirement plans, investments, trading, etc.

CONCLUSION

Machine learning not only eases the financial work of companies but also assist them with better solutions. From managing assets, dealing with fraud, assessing risks, document authentication to providing investment advice, everything is managed by process automation.