How to Drive Growth in Finance and Fintech with Machine Learning

Many financial enterprises have opted for Machine Learning tools to uplift their performance and customer service.

Over the past few years, Machine Learning and Data Science have been garnering attention across the financial sector. According to Gartner’s report, 25% of customer service activities will be handled by AI/ML technology in 2020.

Let’s bring 5 key areas in financial services to light where Machine Learning is being employed successfully:

1. AI Marketing

AI MarketingMachine learning is all set to impact major financial service areas and marketing is on top of the list.

As a financial service marketer, it must be challenging for you to be at the top of your marketing game in today’s evolving consumer behavior world. Carrying out relevant marketing campaigns for your customers to generate revenues is already an intense task. Moreover, outperforming your rivals to stay ahead of the competition becomes the cherry on the top. To overcome these challenges, you require a comprehensive consumer behavior and trend analysis of the financial market.

Here, Machine Learning plays its role by helping you in predictive analytics. With the help of Machine Learning and statistical modeling technology, you can analyze the browser history of your customers. This helps you to identify customers who are relevant to your financial services or products and can therefore lead to higher ROIs. You can craft appropriate marketing strategies based on analytics. For example, it enables you to approve or reject credit card offers and lets you focus on relevant leads.

2. Algorithmic Trading

Algorithmic Trading

The concept of algorithmic trading is not new to the financial industry since a huge amount of data is available for financial institutions that they leverage in predicting stock price fluctuations depending on real-time events. But this technique doesn’t guarantee you accurate stock value predictions due to human coding errors.

With the introduction of machine learning trading tools, you can acquire real-time news from digital platforms and media in an efficient way to improve your prediction accuracy in trading decisions. It helps in high-frequency trading in which you have to deal with a tremendous amount of stock orders at a fast pace.

3. Fraud and threat detection

Fraud and threat detection

Data analytics has been a life-saver for financial managers in terms of fraud and threat management. It lets you detect unusual customer actions and identify suspicious transactions to minimize false-positive customer transactions. The tendency of machine learning to leverage data sites in identifying correlations and patterns in financial variables has largely transformed banking operations.

With Machine Learning, you can surge efficiency in your financial operations like risk assessment and management, fraud and threat detection, and anti-money laundering. This technology provides you with a robust and scalable analytics platform that allows you to take relevant steps in mitigating the risks of fraud.

4. Robo Advisors

Robo Advisors

Financial advisory is a hectic process that often leads to a conflict of interest between the advisor and the client because of never-ending arguments on investing plans between both. By embracing Robo advisors in its operations, investment advising service has become more convenient and accessible for people.

The Robo advisors provide each customer with specific investment plans based on algorithms. The consumer algorithms depend on each customer’s profile, financial and investment plans, and risk tolerance capacity. The automation of advisory services based on consumer algorithms saves you from the expense of a human financial advisor who often doesn’t work in the customer’s best interest. Hence, with Robo advisors, you are better able to build and maintain customer relationships.

5. AI Security network

Machine Learning graph

Considering the huge datasets available to financial enterprises, security and compliance pose the greatest challenge to them. The amount of data is only going to increase exponentially soon which will increase the risk of data breaches and cyber-attacks. If you lack sophisticated security parameters and cybersecurity networks, you are at risk of malicious attacks stealing your value consumer data.

The deep learning algorithms can rescue you from cybercriminals by significantly improving your security protocols and compliance control. With the latest Data Science tools, you can build a strong cybersecurity system that can lead you to identify and shield any cyber attack efficiently.

The new decade is all set to present new challenges with disruptive innovation and digital transformation. The role of AI and ML in every business is crystal clear. The use of machine learning applications in the finance industry however still needs to be strategically planned for desired results before it widely affects the operations, performance, and customer experience.

Data Science/ML engineering at Tkxel

We at Tkxel aim to strategize and engineer your data science and AI transformation initiatives into products that you would love. Let’s talk to drive your strategic business decisions through data science.

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    Jason Frost

    Manager, Customer Success

    571 549 8430