Artificial Intelligence and Machine Learning are currently used in a wide variety of activity areas, and several years ago, these technologies started to actively penetrate the financial sector. They have already proven themselves well when in banking, the mortgage market, the insurance market, and some other areas.
Apps based on machine learning technology have one key advantage — they can self-learn in use and adapt to multiple environments and scenarios. This feature opens up great opportunities for financial businesses and fintech startups.
In this article, we will provide practical examples of machine learning app use cases for financial services, banking business, and fintech projects.
1. Process Automation
That is one of the most popular machine learning use cases in finance. If you replace manual work and automate routine, repetitive tasks using software solutions, you can reduce costs and increase productivity. This way you both optimize your business operation and gain a strong potential for scaling.
Bellow, you can see some examples of financial process automation due to machine learning:
- document flow automation
- employee training gamification
- customer request track and recognition
- customer behavior interpretation and a lot more.
2. Customer Experience in Financial Services
High-level customer support is an important indicator of quality financial services. Machine learning helps companies understand their customers’ needs better and personalize service recommendations and marketing offers.
For instance, the banking industry might use a specific system of recommendations aimed to improve customer savings strategy, choose the best credit card plans, and so on.
It can also be a chatbot that processes customer messages, accepts complaints, and handle personalized service offers.
3. Security & Fraud Detection
Transaction analysis used to detect security threats such as fraud is among promising machine learning case uses in banking. To achieve higher security levels, dedicated algorithms analyze cardholders’ transaction data and other actions.
Such algorithms can detect suspicious activity that potentially threatens account holders, increasing customers’ assets security. Payment systems like Payoneer and government agencies actively use these technologies to prevent money laundering.
4. Management of Credit Scoring (Loans)
Financial institutions can use machine learning to grant loans to speed up loan issuing due to more efficient and faster risk analysis. A well-learned system performs real-time underwriting and credit scoring on its own, helping company employees to make accurate decisions faster and even approving loan applications automatically.
These machine learning use cases are especially valuable for banks. Credit scoring is quite time-consuming as employees have to process large amounts of different customer data. Dedicated algorithms have learned to make quite accurate predictions to let bank employees know if customers are going to return the money loaned or not.
5. Asset Valuation and Management
Asset and wealth management companies should take a closer look at the possibilities that machine learning algorithms can offer to carry out these tasks. This way, investment management can become more accessible, and decision-making speed and efficiency will substantially increase.
6. Stock Market and Algorithmic Trading
Algorithmic trading is not an innovation and has been actively used by financial institutions for many years. Trading algorithms, not people, do almost 90% of Forex trading.
It is not possible to manually reproduce the frequency of trading transactions executed by algorithms. For this and several other reasons, practically every large company operating in the financial sector invests in apps based on machine learning technologies.
Machine learning algorithms can simultaneously analyze data from hundreds of sources, identify patterns, and act proactively, holding or buying shares according to forecasts indicating their possible rise or fall.
Even today, insurance companies use algorithmic analysis to predict the likelihood of an insured event for a particular customer before issuing an insurance policy. The next step for insurance companies to take is to use machine learning technology in scoring models.
Such technologies are commonly used to facilitate the following operations:
- shorter time for payment application consideration
- fund reservation to cover insurance claims
- application evaluation and prioritization
- disputed application claim cost calculation and the likelihood of winning such a case in court.
8. Predictive Analytics
Machine learning algorithms are a promising tool used to predict trends in the financial market. Companies can train their machine learning apps on large amounts of data and in real-time scenarios. This approach allows businesses to react to market changes earlier and more flexibly than when forecasting with traditional methods.
Financial companies can also use machine learning algorithms in early warning systems that predict risk scenarios, financial anomalies, portfolio changes, etc.
Wrapping It Up
Machine learning technologies are gaining popularity in the financial services sector and are more and more used by many companies in practice. They plays an increasingly significant part in various processes and will become more common and widely used in the coming years due to technological developments and new business models’ emergence.
If you believe your fintech company, product, or startup can benefit from machine learning technologies, contact us, and we will help you implement your ideas.