How can Machine Learning improve the banking sector?

Home/Banking, Machine Learning/How can Machine Learning improve the banking sector?

How can Machine Learning improve the banking sector?

machine learning in banking

We are all familiar with artificial intelligence by now, living in Generation Z and seeing self-driving cars in debate and discourse around the world. But are you aware of how machine learning is slowly revolutionizing the world? In this blog, I will talk about what machine learning is, how it works, and seven ways in which it is changing the face and the working of the banking sector. 

What is machine learning? 

The branch of artificial intelligence that allows machines to self learn, improve, and perform activities completely free of human intervention is called machine learning. In other words, when a machine trains itself instead of a human modifying it, the process is machine learning. 

We interact with it on a daily basis without actively realizing we are doing it. For example, every time we read an email and get a number of options clicking on one of which would lead to the automatic generation of a reply is an example of the machine doing work for you without your asking it to. It analyses the content of the email in order to determine what the suggested response should be, in the same way as it warns you with a ‘you talk of an attachment in your email, but there isn’t an attachment; attach?’ dialogue box if you prepare to send an email to your boss but forget attaching the content you had to send. It similarly reminds you to give viewing/commenting/editing access to people you share a google doc with in case you need to. Machines like your smartphones or even some laptops know to protect your data unless facial or voice recognition comes back positive thereby prioritising data over the consumer, a preference that gets inverted once the device is unlocked. All of these are subtle examples of machines acting of their own accord. 

How does machine learning work? 

There are three central routes machines may take or are programmed to take in order to learn. 

  • Trial and Error
  • Supervised Learning 
  • Unsupervised Learning 

Trial and Error: This is a process in which a machine takes a number of routes, no matter how many attempts it takes, in order to come up with the fastest, most efficient way. This is a process of self-learning through analyses of time taken and rewards based on the completion of tasks. The machine learns to optimize time and output and strike a balance between the two. 

Supervised Learning: This happens through the use of labeled data. The description of, say, the same picture across millions of computers all over the world that the said machine has access to leads the machine to come up with the most suitable description. It happens in a similar way with respect to different industries when industrial products are concerned. Different descriptions help industries in optimizing product quality and approachability.   

Unsupervised Learning: Instead of looking for unique data and forming a larger picture, this method focuses on looking for similarities in data and divided people into groups. Division of people into groups also divides needs into groups, and then sectors can analyze which of those needs is being met and which of them they can work on providing. These categorizations enormously help industries in looking out for opportunities that they otherwise might have missed.

The Banking Sector

The banking sector is the most integral cog of the entire financial system of the economy of any country. They accept money from the general public to keep it safe as deposits, lend out money to meet their financial needs in the form of loans, enable the transfer of money from one place to the other through remittances, and lead to crucially important credit creation. They act as trustees and either carry out or facilitate government business such as a collection of tax or distribution of benefits. They bring with themselves financial products such as insurance, mutual funds, NPS, etc. They provide financial inclusion and release underprivileged people from the clutches of greedy money lenders. Qualitative benefits the formal banking system of a country induces its public and public space are manifold. They foster a habit of saving in their people, facilitate capital formation thereby making it easier for businesses to thrive and contribute to the self-sufficiency of a country, promote agriculture by making it easier for the farmer to acquire raw material, encourages small industries and self-support community groups like rural women collectivizations in various villages, help in a balanced growth of the country by going into special economic zones (SEZs) and bringing the benefits of banking to inferior places and enhance foreign interaction. 

Machine Learning and Banks 

With the bank meeting so many needs of such a large mass of people, their requirement for assistance is real, and artificial intelligence and machine learning are there to provide just that. Here are seven ways in which machine learning helps banks. 

1.Risk mitigation through predictions 

The analysis of broad digital footprints using AI’s ability to quickly sift through enormous amounts of data and machine learning- applying the advanced mathematical tools at its disposal to perform complex operations and produce results banks are looking for- leads to financial forecasting that could never be imagined before. This sort of forecasting and the use of machine learning-enabled recommendation stations to help banks with an accurate assessment of potential borrowers. They also give them the lead in investment decisions by opening up whole new avenues of computational opportunities. 

These predictions help the customers as well in the same manner, thereby making the bank better advisers. Analysis of the market, particular businesses, or certain investment opportunities enables banks to give better advice to their customers, minimizing the possibilities of incurring financial losses at a later stage. 

2. Consumer footprints leading to unprecedented customization

The financial status of a customer, thanks to artificial intelligence, can be checked across multiple accounts in a matter of seconds. It is not just account statuses, though. Complete transaction histories, details of every previous contract, small technicalities of every past interaction, all of these are revealed at a pace humans cannot even dream to achieve. Data can be pulled out from social media, websites, newspapers, and other sorts of media. This process has the ability to streamline consumer experience like never before. The banks know what their customers need and can give it to them in a form that appeases, increasing their goodwill. 

3. Categorisation and identifying needs 

With machine learning (through the unsupervised learning we discussed above) cross-referencing people and grouping and regrouping them according to what they need, banks can spot opportunities they were not being able to see with people analyzing data. When banks see a number of people having similar needs, they can formulate plans that would satisfy these needs. Had the process been carried out with the help of surveys, it would have been cumbersome and too time-consuming to be efficient. Now it is much simpler to come up with product, policy, and service that would cater to a group and increase both services available to the public and capital and profits of the bank. 

4. Adaptation to new developments

Machines do not have emotions. Even if large fluctuations happen in the market or there are unpredictable changes that have destabilizing potential, destabilizing machines are still difficult, especially fast and intelligent machines (that employ machine learning) that adapt to changes as quickly as they happen. 

5. Minimising fraud 

With the sifting and data pulling abilities discussed in point 3, it becomes a matter of seconds to pull up all possible financial information about a client. Smart machines can spot suspicious activity or uncharacteristic behavior and alert managers. This helps in tracking down anybody performing fraud, but also as a deterrent. Now that people know it is not bored people staring at excel sheets but computers that never get tired keeping an eye over their transactions, they are less likely to indulge in scams like corruption or laundering. 

6. Human Resource Preservation 

The minds at work are not wasting their energy or time in doing cumbersome, time-consuming jobs. All that can be automated is being automated and instead of getting wasted, human resources can be employed wholly in deliberating over ways to enhance the system. This has the natural consequence of an overall strengthening of the system. 

7. Elimination of Bias

Last but in no way least, machines eliminate the bias humans may not be able to fully remove from their minds. In countries such as the US where people of color are looked down upon, or places like India where some people might not be considered worthy of receiving, say, a loan because of the ethnic, cultural, or religious minority or economic background they might be coming from, it is simpler for these communities when it is not humans who grew up learning and applying prejudices but machines that implement an algorithm that does not discriminate that give recommendations and come up with reports. Decisions the bank then takes would stem from a place of ethical determination of potential instead of human bias. Of course, people still might let their biases get in but then they would have to give answers when questioned about their decision of not taking the optimal call as determined by the algorithm. This reduces propensity. 

These are some of the ways in which technology has been benefiting banks and in turn the financial sector and further, in turn, the country. With advancements we have every reason to believe will happen (as technological evolutions happen) these mechanisms will be further enhanced and keep giving their returns to the people.

 

By |2020-10-21T10:48:16+05:30September 21st, 2020|Banking, Machine Learning|Comments Off on How can Machine Learning improve the banking sector?

About the Author:

Sonal Mehta is a Content Lead at Solulab, USA based leading Blockchain Technology, mobile apps and software development agency, started by Ex vice president of Goldman Sachs, USA and Ex iOS lead engineer of Citrix. Solulab help build startups - we are a no-sweat technical partner for early stage entrepreneurs to launch ideas from scratch and for later stage startups to build more quickly and affordably.

about

Get to know us.

arrow

Say Hello !