5 Applications: Artificial Intelligence & Machine Learning in Banking

Artificial intelligence (AI) has brought upon one of the biggest revolutions in the banking industry. Where traditional software was hard-coded with rules to define the area of execution, artificial intelligence allows computers to create their own rules based on the guidelines and data fed into the system. This means that now computers can move a step ahead from rule-based to logical thinking and reasoning. Being one of the evolving technologies we can see rapid innovations in this space.

Businesses across all industries are working to leverage its power to improvise or even transform their current process. In this evolving sphere, the banking, finance, and insurance industries are taking best possible advantage of AI and machine learning. Here, we will analyze some of the prominent applications of AI and ML in banking and finance.

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5 Applications of Artificial Intelligence and Machine Learning in Banking and Finance:

1. Fraud prevention:

Securing the money of clients is one of the primary function of banks. We rely on banks to securely execute our transactions. Hence when machine learning first came in, it was mainly initiated in securing the existing banking infrastructure. Traditional algorithms could only catch a fraudulent transaction when it violated some of the pre-set rules.

With machine learning, we can go a little deeper and identify suspicious activities based on the transaction history and behavior of individual customers. For instance, if a huge transaction is initiated from a bank account with a history of performing minimal checks, machines can immediately withhold it until it is verified by a human. The fundamental benefits lie in the fact that machines can perform such analysis in real time and can also learn from the results of past actions.

Read More: Artificial Intelligence vs Humans - Who'll Win?

2. Chatbots:

Chabots are artificially intelligent software that can stimulate a human conversation. These bots use a technique called natural language processing to understand human inputs (voice or text) in contextual terms and respond accordingly. Bankers need to address customer queries on a large scale.

Most of these queries are similar to each other and hence offer scope for automation. Before advancements in machine learning, it was very difficult to make a computer understand human queries in the right context. Now we have chatbots that can take up all these tasks thus helping bankers focus on only the critical customer issues. One popular use case of such bot is the recent initiative by HDFC Bank to launch its very first chatbot, ‘Eva’. This effort gained tremendous media coverage and helped HDFC serve many of its clients.

3. Risk Management:

While providing a loan to any client banks go through a process of risk assessment to estimate the creditworthiness of a prospect. Traditional systems relied on historical data like transaction history, credit history and income growth over years to understand the risk associated with every loan extended. This resulted in inconsistent estimates as historical data is not always an accurate standard to predict future behavior.

Then came machine learning to the rescue. Machine learning allows analysis of real-time data of recent transactions, market conditions and even latest news to identify potential risks in offering credit. With the help of predictive analytics, an ML algorithm can analyze petabytes of data to understand micro activities and assess the behavior of parties to identify a possible fraud. This is something impossible for human investors to perform manually.

4. Marketing and Support:

With the ability to analyze past behavior to optimize the present future campaign, machine learning is an influential tech for marketers. By analyzing inputs from various data sources like behavior analytics from mobile and website, recent transactions, response to ad campaigns etc. marketers are able to craft targeted campaigns. They can also map the entire consumer journey from first interaction to purchase using clever attribution models.

The modern customer’s journey goes way beyond the initial account set up especially in banks. This makes it important for banks to provide best in class customer support. Unfortunately, traditional CRMs and ticket management system fail to solve unique problems. Machine learning algorithms can learn from their past actions and hence improve the quality of service thus, aiming for best possible customer experience.

5. Algorithmic Trading:

Artificial intelligence allows the analysis of varying complicated market factors at the same time. Today there are many Hedge funds across that leverage high-end systems to deploy AI models which learn by taking input from distinct sources of financial markets and sentiments. This enables real-time decision making by eliminating the time gap between insights and data collection.

While the strategy of investment remains different for each fund, the background technology of AI remains the same. Ultimately most of the high-frequency trades (HFTs) can soon be automated by identification of trading opportunities based on the inputs. Some popular hedge funds using AI include - Two Sigma, LLC, PDT Partners, DE Shaw, Man AHL, Citadel, Vatic Labs, Point72, Cubist etc.

While having a high-level idea of technology seems to help, we need a lot more detailed insights when we go into actual implementation. At NewGenApps, we have established in working on various AI technologies including chatbots, recommendation engines etc. Feel free to get in touch in case you have a project or need consultation.