Machine Learning in Financial Services

Machine learning is a subset of artificial intelligence that is used to make descions on basis of data. In financial services, it can transform business processes related to chat bots, personal finance, and security and risk management. Here are some real-life applications of machine learning in financial services.

Applications of Machine Learning in Financial Services

Machine learning has had helpful applications in finance well before the advent of mobile banking apps, AI chat bots, or opimised search engines. Given high volume, accurate previous records, and quantitative nature of the finance world. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible power and more accessible machine learning tools.

Customer Service

Chatbots are being deployed in financial services to automate common customer service requests, answer the questions that are asked frequently, and can help with your transactions and payments. They use natural language processing to understand what a person is trying to say and give an appropriate response in return. However, chatbots can sometimes struggle and go off-script from specific instructions they’ve been

programmed to follow.

Machine learning helps chatbots assimilate information from each interaction and “teach” them how to respond in the future. For example, machine learning can teach chatbots how to identify frustration and anger and adapt subsequent responses. This might mean replying with calming language designed to diffuse the situation or perhaps redirecting the frustrated customer to a live agent.

Machine learning can also be used to improve the customer experience and FAQ. For example, if a customer tends to pay the minimum amount due on their payment at the latest possible date, this behavior might indicate that the customer is facing a cash-flow crunch. Machine learning can identify these patterns and offer the customer a different payment methods, or even a personal loan to help improve their ability to make on-time payments.

Personal Finance

Budget management apps powered by machine learning provide customers the benefit of highly targeted financial advice and guidance and about loan schemes. These apps allow customers to track their spending on a daily basis using their mobile devices. Machine Learning can also learn their spending patterns and can give suggestions how to same money

Machine learning helps bank identify struggling customers and provide better options for serving their needs. When the system detected unusual spikes in late-night credit card usage among certain customer segments, combined with low savings rates, the bank was able to determine that these customers were facing some level of financial imbalance. To reduce the risk of defaulting, customers tagged by the system were given credit limit increases and offered free financial advice automatically.

Portfolio Management

The term "robo-adviser" was essentially unheard-of just five years ago, but it is now common place in the financial savings and investments.

Robo-advisors are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user. Users enter their goals, age , income and current financial assets. The advisor would more accurately be referred to as an then spreads investments across classes and financial instruments in order to reach the user's goal

Fraud and risk Management

Financial services organizations handle a large amount of customer and transaction data that is scanned for fraud. Payments, in particular, are a hotbed for fraud activity and organizations are constantly looking for new ways to prevent fraud and risk management protocols to lower the risk.

Looking Ahead

As the financial services industry continues to leverage machine learning and predictive analysis, the volume of data these firms generate and store is increasing. Protecting that data, other sensitive assets, and business operations will only become more

challenging as the data is increasing at high rate. Firms will have to adopt new security technologies that can enhance their security and lower risk.

Algorithmic Trading

In algorithmic trading, machine learning helps to make better investing decisions. A mathematical model monitors the news and trade results in real-time and detects patterns that can force stock prices to go high or low. It can then act proactively to sell, hold, or buy stocks with its predictions.

Machine learning algorithms can analyze thousands of data sources

at a time, something that human traders cannot possibly achieve.

Machine learning algorithms help human traders squeeze a slim advantage over the market average. And, given the vast volumes of trading operations, that small advantage often gets into significant profits.

Security 2.0

Usernames, passwords, and security questions may no longer be the norm for user security in five years. User security in banking and finance is a particularly high stakes game. In addition to anomaly-detection applications like those currently being developed and used in fraud, future security measures might require facial recognition or biometric data.

Marketing

Having read of the many ways in which machine learning can keep accounts secure, improve risk management, and offer investment strategies, you might not expect the technology to also be a good marketing tool. On the contrary. The ability to make predictions based on past behaviors is fundamental to any successful marketing effort. By analyzing web activity, mobile app usage, response to

previous ad campaigns,social media , machine learning software can predict the effectiveness of a marketing strategy for a given customer .

With the online marketing power of Google, now augmented by machine learning, it is possible for developers working in the financial sector to create smarter tools that make the job of marketing executives easier than ever.

Network Security

Among the top considerations for any network admin or data security professional is how to recognize suspicious patterns occurring across their networks. The challenge to identify such patterns lends itself perfectly to the capabilities of machine learning. The power of intelligent pattern analysis,, certainly gives machine learning technology an edge over traditional, non-AI tools. One might go and declare machine learning as the last hope of securing critical networks against professional and state-sponsored cyber attacks.

High-Frequency Trading (HFT)

Algorithmic trading that happens at high speeds beyond human capability gave rise to high frequency trading (HFT) – that is hundreds of thousands of trades per day executed by machine learning complex algorithms that analyze multiple markets to execute orders based on market conditions.

HFT is a subset of algorithmic trading and an excellent application of machine learning in finance.