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Machine Learning helps in financial sector

How can Machine Learning help in Banking

With the technology being more savvy by leaps and bounds, we can now ensure a new technology paving new avenues and being tech ready it becomes more important to understand the impact of AI (Artificial Intelligence) & ML (Machine Learning) on the different sectors. The skill of the computer programs to learn on their own and improve over time creates new opportunities for industries across the board.

Use of ML (Machine Learning)

1. Risk Management:
The last 2 years have witnessed a huge change and also turbulent times with a lot of changes happening on the banking front. The solutions used are mostly on the ML front. Traditional software when used help in predicting the creditworthiness which is based on the information from loan applications and financial reports. With the help of the ML technology, it can go further and also help identify the current trends which can help their client to pay accordingly. With the help of the risk management using ML it can help prevent the fraud, financial crime and crisis predictions too.

2. Predictions for investment:
In the last couple of years the hedge funds have moved away from traditional analysis methods. Replacing this, they have adopted the ML (machine learning) algorithms which are used for forecasting fund trends. Using ML the fund managers can identify the market changes in an earlier trend as compared to the older systems. We have recent examples like JPMorgan, Bank of America, and Morgan Stanley who are developing automated investment advisors, powered by machine learning systems.

3. Customer Service:
Poor customer service or improper customer service remains one of the major5 complains amongst the financial clientele. It doesn’t matter whether are speaking with a human or a bot, what matters is how accurate info do we provide to the customers by solving their problems quickly. Use of ML puts the entire thing on a new front when the virtual assistants are being enabled to learn rather than just following the steps or the orders which results in an improved customer experience.

4. Secured network:
The challenge here is to help secure the network with the help of the technology. ML is used here for protecting financial data using sophisticated cyber – attacks which help to protect the software. To meet the current growing secured needs of the financial data around, the ML solutions are ready to deploy with the help of an intelligent pattern analysis, combined with big data capabilities, gives ML security technology an edge over traditional, non-AI tools.

5. Trading using algorithms:
When referring to an algorithmic trade, one means that the trading can itself buy or sell the stocks or shares when the price-per reaches a specific level. ML technology offers a complete new and diverse form of tools to make the trading look more automatic and also makes it intelligent too. These are designed keeping in mind the historical market behavior, which helps in determining an optimal market strategy which makes trade predictions.

Using the technology one can make efficient use of using financial knowledge with the help of using ML for all other financial purposes keeping the sector more up to date and more intelligent and keeping the sector always on an upbeat. To know more on how can one make the best use of the ML in different sectors please write to us in


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