Artificial Intelligence (AI) has been introduced into many aspects of our lives, with the finance industry among them. AI in finance can be used in various ways, such as analyzing large amounts of data or automating time-consuming processes. It can also be used to recreate the human-style decision-making process.
We’ll look at how financial institutions are now using AI systems to streamline their processes. Find out how AI finance applications are quietly revolutionizing the financial service industry.
What is AI in finance?
To understand the use of AI in the financial industry, we can look at a few of the key terms and what they mean. AI could be described as a tool with the ability of a computer system or program to think and make decisions.
Machine learning models are where AI tools can find patterns that allow them to improve over time. It means they can start doing things they haven’t been explicitly programmed to do. Machine learning in banking is used to learn about customer behavior, among other factors.
Automation is where technology is used to carry out repetitive tasks in AI financial services without the need for human operators to get involved. As for predictive analysis, we could say it involves using historical data to forecast what might happen next.
Each of these AI technologies can be used in financial operations. We’ll see in the next section how they lead to a series of advantages in different situations.
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Benefits of AI in financial services
The financial sector can leverage AI in many ways. Here are some key benefits they offer.
Faster decisions. The ability to make fast, accurate decisions is one of the most interesting aspects of AI-powered tools. It is crucial for financial teams that need to constantly make the right decisions in high-stakes matters.
Automation. A high percentage of operational costs in the financial industry comes from repetitive tasks. By having AI handle them, we achieve greater operational efficiency.
Lower costs. Building on the idea of cost efficiency, there are numerous areas where cost savings can be achieved through this technology.
Higher accuracy. Dealing with vast amounts of data can lead to costly errors. It is another point where finance AI tools can prove invaluable.
Better fraud detection. Rather than relying on human detection, AI-based risk assessment can identify trends, making fraud easier to spot.
Most common use cases of AI in finance
By considering the previous points, we can see how AI can be used across different financial processes and systems.
Fraud detection. Modern financial systems need to analyse a massive number of data points and transaction patterns. It provides more accurate results from fraud detection systems and fewer false positives.
AI risk management. AI fintech solutions cover all areas of risk management, instantly assessing relevant customer data before making a decision. It means that credit risk and other vital areas are carried out effectively.
Automated onboarding. It is the way that new customers can open an account without any delay or friction. Computer vision, natural language processing (NLP), and biometrics are all used to allow new accounts to be opened smoothly.
Payments automation. Dynamic pathing and optimization are used to make payment processing as secure and cost-effective as possible. It includes smart detection and anomaly detection.
Transaction monitoring. Rather than simply flagging suspicious behavior, AI can now give insights to financial organizations. It does this by focusing on the context and intent of every transaction, looking for relationships between seemingly unrelated accounts.
AML/KYC. AML (Anti-Money Laundering) and KYC (Know Your Customer) are areas where AI-powered systems have made a key difference. They continuously analyze data for AML and KYC in finance, so the human team only has to focus on the most urgent cases.
Credit scoring. This aspect of financial organizations has evolved from being a simple look at a person’s past finances to something that can predict their future situation. Trended and alternative data are used to provide a fuller picture of the credit risk a mortgage or credit card company faces.
Chatbots. These bots have evolved from being simple chat assistants to becoming banking assistants. They can now carry out various processes rather than just answer simple questions.
Investment management. This area now incorporates machine learning tools that can research and execute personalized financial strategies based on each investor’s goals and the global situation.
AI in banking
The arrival of AI solutions has shaken up the banking industry in several aspects.
Compliance
It includes the regulatory compliance aspect. Autonomous AI compliance agents continuously monitor the latest regulatory updates to inform updates to the company’s risk controls.
Loan approvals
In this case, the use of AI by financial institutions enables faster, more accurate credit decisions. AI systems can look beyond credit scores to assess real-time data from open banking and alternative data sources. It means that personalized pricing is also possible.
Digital identity
The use of 3D biometric checks in the financial services industry has ensured that identity verification is slicker and more trustworthy. Fraudsters are identified using deepfake defense techniques that make bank ID checks virtually impossible to fake.
Process automation
AI bots speed up the loan process in financial institutions by automating tasks. Once the customer starts the loan process, AI systems handle all the basic financial functions needed to complete it.
Customer support
Rather than simply redirecting loan-related questions, artificial intelligence bots can now answer questions and provide tailored financial advice. They can also assess credit risk and perform the steps needed to complete the interaction.
AI in payments
When we turn to the use of AI in payments, we can see that AI applications are now vital in the smooth processing carried out by financial institutions.
Automated fraud scoring using AI for fraud detection. Every transaction can be assessed by artificial intelligence and immediately awarded a score.
Transaction monitoring. AI models can see the full payments in a way that human intelligence can’t manage. It allows them to monitor all transactions and look for relationships that need to be flagged or assessed.
Reducing false positives. Another of the finance functions carried out by artificial intelligence is to assess your behaviour. With machine learning, it can instantly determine whether additional information should be used in future transactions.
Real-time approvals. The need to wait for approval has been virtually eliminated by artificial intelligence. All of the work traditionally done by finance professionals is now completed instantly.
AI and fraud prevention
Advanced algorithms and AI models are also used in fraud prevention processes.
Anomaly detection. The risk assessment is carried out using financial AI to determine the context of each transaction and situation.
Identity theft. Liveness checks are performed by artificial intelligence to securely verify the client’s identity.
Pattern recognition. Another way to leverage AI is to look for unusual patterns that suggest organized crime or identity theft.
Behavioral biometrics. This element of implementing artificial intelligence in financial institutions examines how users perform activities. It includes the way you use your smartphone and your navigation habits.
Limitations and risks of AI in finance
While AI and machine learning are helping finance teams work more efficiently, some concerns need to be addressed for this approach to continue advancing.
Perhaps the most urgent issue is privacy. Since AI needs a huge amount of financial data, this is an area that needs to be closely controlled. We also need to ensure the data is accurate.
Worries about potential AI bias are another concern. If AI models are trained on a specific segment of the population, this may be reflected in the decisions they make in the future. Different authorities have introduced regulatory frameworks, such as the EU AI Act, but this means that an overall global approach is lacking.
Finally, finance professionals may struggle to explain decisions made by AI and predictive analytics to customers. The finance teams that speak to customers can’t always see why a certain decision has been made.
Future of AI in financial services
A few important trends are worth looking at more closely for the future.
Open banking AI. Rather than just fetching data to read, artificial intelligence will ensure that open banking data is used correctly.
Instant payments. This technology will make transfers faster and more seamless than ever before, immediately checking all the factors for instant approval.
Real-time fraud scoring. All relevant factors will be assessed in real time, allowing the right decision to be made on whether this poses a fraud risk.
Automated compliance. AI agents can continuously monitor cash flow trends and behavior, ensuring compliance at all times.
AI-powered risk analysis. Your credit history is among the many factors that can be considered.
Need a reliable provider? Try Genome
Genome uses real-time transaction monitoring and automated checks to help detect unusual transaction patterns and reduce fraud risk. We also use transaction risk scoring as part of monitoring, supported by automated tools and manual compliance review. For onboarding, Genome partners with AI-powered identity verification and automated KYC/KYB/AML providers.
You can open a business account that gives you a simple approach to ordering physical or virtual cards. With a dedicated IBAN account, you can easily send and receive SEPA Instant and Credit Transfers.
Additionally, we provide merchant accounts and services, allowing you to accept payments from customers.
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