7 Finance AI and Machine Learning Use Cases

Finance AI technologies have changed the lives of millions of people worldwide by providing 24/7 access to bank accounts, professional support, and fast and easy operations with financial apps resulting from the implementation of artificial intelligence in finance. And now, the explosion in Generative AI has opened up new vistas for financial services organizations to explore. This article will outline the latest use cases of AI in finance and define the technical aspects of applying machine learning (ML) to the banking industry. Let’s start!

Implementing AI Tech Solutions

Artificial intelligence (AI) technologies have brought huge changes to society, and the revolution is still ongoing. Initially, they affected the following five industries:

• Retail
• Finance
• Transportation
• Healthcare
• Advertising

The banking, retail, and healthcare sectors have made the biggest investments in AI technology development.

The aim of artificial intelligence technologies is to develop smart software solutions, technologies and machines that can perform actions and make decisions like humans. Artificial intelligence can learn, analyze, plan and carry out human functions. Furthermore, AI can improve itself without human help or developer efforts. It builds the model of behavior based on input data and its own experience.

The development of AI brings incredible changes. One of the most important ways is that AI analyzes information, solves tasks and carries out operations more quickly than any human could.

Technological Aspects of AI

Artificial intelligence includes four main technological aspects. A few of them are sometimes considered to be synonyms for artificial intelligence.

• Artificial Intelligence is a broad field of computer science focused on creating systems capable of performing tasks that would normally require human intelligence. It’s important to recognize that the broad umbrella term “AI” encompasses far more than a few headline-grabbing apps.
• Machine learning, a subset of AI, involves algorithms that allow computers to learn from data, recognize complex patterns, and make informed decisions and even predictions. In combination with other powerful AI technologies, machine learning powers the most impressive modern examples of AI, such as facial recognition software and predictive analytics engines.
• Generative AI, another subfield, specializes in creating new data that is similar to the data it was trained on. It is especially known for natural language processing (NLP) which can power incredibly human-like chatbot technology. The best-known example is ChatGPT

In the financial sector, these technologies are more than just innovative concepts; they are essential tools for survival and growth. They enable financial institutions to automate tasks, analyze large datasets, and offer personalized services, thus enhancing efficiency and customer satisfaction.

AI in the Financial Sector

According to the Cambridge Centre of Alternative Finance:

• 85% of financial services providers are currently using AI.
• 77% believe AI will be essential to their business within two years.
• 52% have created AI-enabled products and services.
• 50% see AI as a competitive threat.

Ensuring data privacy and security within the banking industry is of utmost importance. Financial services executives have the ability to bolster customer data protection and improve operational efficiency by leveraging AI and machine learning in various ways:

• Leveraging machine learning algorithms to identify and thwart fraudulent activities and cyber threats
• Implementing biometrics and computer vision technologies to swiftly authenticate user identities and streamline document processing
• Incorporating intelligent solutions like chatbots and voice assistants to automate fundamental customer service tasks.

Acting promptly and decisively in embracing these technologies is essential for banking leaders to stay ahead in a rapidly evolving landscape.

Use Cases of AI in Financial Services

Artificial intelligence for financial services automates processes and replaces human decision making. What does it mean for practitioners? Let’s do a review.

Fraud Prevention

Banks and financial organizations deal with huge volumes of personal data as well as people’s money. Fraud is the most dangerous thing in this industry: one mistake can lead to unbelievable losses, troubles and liabilities. When we speak about fraud, we mean credit card fraud, money laundering, and other forms of financial breaches.

A key aim of AI in financial services is fraud detection. AI detects suspicious activities, provides an additional level of security and helps prevent fraud. In short, AI improves bank security.

Trading Algorithms

One of the common problems in trading is getting market analysis too late to take advantage of opportunities. AI finance tools can outperform human trades and bring faster and better decisions on trading. Also, the comprehensive analysis of different market aspects and factors allows banks to achieve new heights in trading algorithms. Nowadays, a lot of well-known hedge funds use AI for these purposes. The technology is quite popular for data science as it helps a company build its trading system.

Risk Management

Artificial intelligence in financial services makes a huge difference in investment management and risk analysis. AI can accurately estimate the client’s creditworthiness and answer the crucial question: is this person reliable? The AI-based system analyzes the risks by considering transaction and credit history, income growth, market conditions, etc. Predictive analytics provides considerable details on micro activities and behavior to determine if investments should occur.

Similar abilities can be brought to bear on the insurance side as well, helping to support underwriting with fast, efficient analysis and decision making.

Customer Service (Chatbots)

Customers want to have a connection with their bank 24/7 (not a surpise). And finance AI technologies can assist with this by running chatbots.

Banking services are enhanced by simulating a real conversation. Сhatbots in financial services using natural language processing technology answer customer queries in real-time and precisely. That means a lot of extra attention, new clients, and better conditions for the current ones.

Robo-Advisory

The considerable interest in passive investment makes fintech companies invest in AI solutions. Robo-advisory is based on providing recommendations based on investors’ individual goals and risk preferences. Finance AI automates the investment process so that the only thing investors need to do is deposit money into an account. The most significant benefit of using this tool is offering the ability for people not familiar with finance to make investments. You don’t need to do financial analysis to succeed in passive investment! And it is also cheaper for financial institutions to have robo-advisory than human asset managers.

Regulations and Compliance

A financial institution must comply with different laws and rules that are sometimes even hard to keep track of. Reports take too much time, and one tiny detail missed by a bank specialist may lead to minor complications or even serious problems. AI takes into account all the regulations, detects deviations, analyzes data and follows the rules accurately. Thanks to the complete automation of the processes, it is possible to avoid issues with the help of AI.

Process Automation

Artificial intelligence finance tools can offer massive support in process automation. There are a lot of tasks that are usually done manually by bank employees. For example, preparing documents or accessing information for clients.

Everything can be done in a few minutes or hours with AI automation. Another example of process automation with AI is the ability to verify personal ID. You often need to submit your ID and take a photo of yourself to be confirmed as a user. AI can check the match between an ID and a picture while examining that the ID was not used for fraud.

Final Thoughts

There are tons of opportunities to use artificial intelligence technologies in financial services. All of them aim at the process of automation, improving the customer experience, and elimination of the necessity to involve human action and effort.

AI-based tools for financial institutions can have a huge positive impact. But, there are challenges and roadblocks in the way as well. But don’t be frightened by the potential challenges. With knowledge and expert advice, you can reap the benefits of AI in financial services while avoiding the pitfalls.