Working with machines is nothing new for financial sector. Over 73% of everyday trading is already done by algorithmic machines.
The level and volume of every day trades is so high that it’s impossible for humans to handle it, no matter how many work on the system.
Finance sector is also one of the earliest adopters of Machine learning. Back in 1980s, machine learning algorithms were put to use for stock predictions.
Although, the growth of technology in the sector has been pretty stagnant since the last few decades, all the major banking firms and fintechs are heavily investing in machine learning. Why?
How Machine Learning Can Improve Operations in Financial Sector?
1.Preventing Fraudulent Transactions
Americas lose over $50 billion dollars annually due to banking frauds. The old methods of preventing frauds aren’t working. The sophisticated methods used by criminals cannot be tackled by a human taskforce.
With the help of machine learning services, it’s possible to compare every transaction to the past transactions of the customer and detect whether the current transaction is legit or not.
The system takes the entire history of the customer transactions into account and makes decisions momentarily. It can either delay the transaction until a human reviews it or block it altogether.
As the methods of theft keep changing, the algorithm learns and adapts itself in real-time. In finance sector, it’s crucial to protect the clients from such frauds and machine learning powered systems are the most effective solution till date.
2. Risk Management
A human would try to spot rogue investors on the basis of static information available to him. A self-learning system would be able to recalculate an asset’s worth on the basis of current market status.
Such real-time predictive analysis enables almost precise predictions and also improves the efficiency as the burden of analyzing several documents and accounts is handled by the system.
By employing such ML algorithms for credit portfolio management (CPM), companies can predict which client is about to become a loan defaulter or is going to cancel the deposits.
Such insights are invaluable for financial institutions and can save them lots of money.
3. Market Predictions
Use of machines to predict market trends has been around for decades but at present, their use and scope has increased manifolds.
Investors can instruct the machine to buy or sell stocks on their behalf depending on pre-fed limits.
Through automated analysis, machines can also make recommendations. The preciseness of these is questionable because analysing the stock market is a much-complex problem than we may imagine.
4. Customer Service
Importance of customer service remains the same, irrespective of the industry. In our post about use of chatbots for marketing, we discussed how machine learning powered bots are revolutionising the customer service offered by brands.
Machine learning can help reduce customer waiting time when they call for support, and can also direct them to the correct department without any human intervention.
In fact, deploying chatbots for customer service can save upto $27 billion, spent on customer service in the investment & finance sector.
5. Digital Assistant
Fine management is important in the financial sector, as a delay of minutes could cause major damage. Digital assistants are emerging as a popular application of machine learning, anyway.
However, the popular assistants developed by companies like Amazon, Facebook, Microsoft and Google are good but they are very limited.
In the finance sector, the functionalities like pattern recognition, ability to handle big data, speech recognition, and ability to integrate with third party applications blended into a custom digital assistant could be invaluable.
6. Network Security
Modern day cyber attacks are too sophisticated to detect before they have done major damage. Some could go undetected for months.
Add in a layer of machine learning powered security over your existing security setup can help you strengthen your defence against security attacks.
7. Compliance Checks
Financial institutions are required by law to ensure proper compliance according to several acts and rules.
The compliance team needs to track and analyse all the information regarding the customers and keep it updated. Add to it all the new additions like some new merger or acquisition and it becomes a very complicated and complex problem.
By implementing machine learning algorithms, all these documents and information can be brought together, and stored in a centralised manner making it easier for the compliance team to do their job. It will also decrease the chances of mistakes and non-compliance issues.
8. Wealth Management
Not all rich people know how to manage their money. Deploying a machine learning algorithm to manage their wealth and deal with the risky assets can lead to quick actions on the available information, reduction of risks and overall, better portfolio management.
Having said that, there are very specific challenges the machine learning in finance has to face.
Challenges in Deploying Machine Learning in Finance
1. Data Distribution
Machine Learning is dependent on data sets. The larger the data set is, the better a machine learning algorithm gets at predictions. They can detect patterns and can guess what to expect.
For example, the image recognition system by Google has a dataset of dog images and can then, sift through a set of data and find dog images. It knows it has to find dog images.
But with financial sector, you don’t know what to expect in future. The machines cannot predict a trend that has never been seen before, and that happens often in this sector.
The system can make predictions how future trends maybe like, but they’re far from being accurate.
2. Small Size of Data Sets
The data sets need to be huge for the algorithm to work properly and emulate human intelligence. Data sets in the sector are big enough but some like unemployment rate, and such labor statistics.
It’s possible to tackle this problem by combining data sets but it’s still a problem.
3. Tough to Quantify the Data
We need to feed the financial data to the machine in the language it can understand. However, it cannot be done entirely. For example, you cannot fully explain why great depression in 1930s happened, in a quantifiable manner.
4. Similarities with Recommendation Systems
One may argue that finance predictions are basically ML powered recommendation systems. In a way, they’re. That very fact opens a pandora’s box of limitations - low accuracy, excessive noise, challenge like facing seasonalities, unseen trends and having to combine different data sets for model training.
The advancement in this technology has been rapid in last few years, and surely enough, these challenges are diminishing day by day.
The financial sector has always been open to new trends and machine learning is one of them, the one that will change the entire sector; making it crucial for every financial institution, small or big, to pay attention to machine learning.
All in all, machine learning in finance is a trend to watch out for. If you think you’re already ready to adopt it, you must get in touch with us at Hureka Technologies.
