Artificial intelligence in finance: Top advantages and use cases 

Analytic capabilities give artificial intelligence a crucial meaning for the finance industry. AI helps automate daily tasks and improves decision-making all throughout. Besides, it helps enhance security measures and lower fraud risks. This article gives a closer look at AI’s role in today’s financial organizations.  

Let’s dig into its capabilities, benefits, and challenges you may face from the perspective of DICEUS’ own expertise in AI involvement within the finance sector. 

The role of AI in the finance industry

Global research shows that the adoption rates of AI in finance continue to grow restlessly. As we can see, 46% of the surveyed companies and startups preferred widescale AI adoption in 2022. In 2025, AI is expected to enhance its effect on finance and become a critical implementation.  

But what makes AI so important for the financial industry?  

Artificial intelligence offers a combination of modern tools for ultimate efficiency improvement. It speeds up and simplifies data analysis, forecasting, management, and customer support services. AI replaces routine banking operations with automated ones. It guarantees round-the-clock access to bank accounts. It also redefines risk management and cybersecurity, with advanced fraud detection and anti-money laundering algorithms. 

Use cases of artificial intelligence in finance

The most widely used functions of AI in finance are data analytics and data processing. The reason for this priority is the capability to handle large data volumes. AI can work with everything from transactions to regular reports. It can uncover hidden trends and patterns, which is crucial for market predictions and indication of potential risks. But these examples are only a surface of AI’s capabilities, so let’s have a closer look at other use cases. 

Artificial intelligence in personal finance 

AI provides powerful improvements in financial self-management. In particular, AI can power: 

• Digitized superbanks, where bank clients and regular smartphone owners get to control their financial operations with the help of automated analytics tools. 
• Automated investment platforms offering financial help and advice based on fine-tuned algorithms.  
• AI-powered finance management applications that provide deep analysis of spending patterns to help users stay within the budget line, research credit history, and find other ways to improve credit scores.  
• Traditional banks also develop personalized assistants for real-time financial help and responding to users’ requests. 

Let’s have a look at Erica by Bank of America. Erica is the first major AI-powered financial assistant launched by an institution as large as the BoA. This application provides users with personalized financial guidance. One more of Erica’s advantages is simplifying banking operations. AI offers proactive insights based on habits and goals. Such an app’s function helps users to control their financial situation. Erica helps check the balance in all accounts. AI also sends reminders when users need to make scheduled payments. 

Artificial intelligence in consumer finance 

AI-powered systems study transactions and user behavior to cut the risk of fraud. They can detect suspicious behavior on the account and prevent unauthorized activities. AI in consumer finance is also involved in customer support. It is focused on speeding up customer interactions and helping with common questions. AI solutions offer personalized offers based on spending patterns. 

Mastercard’s fraud detection systems show valuable AI input for the financial industry. The new AI-powered system is designed to enhance card protection. It scans transaction information within cards and merchant data and reacts to fraud signs. This helps increase identification speed for risky or compromised operations. Such an approach doubles the detection rate and makes the system more reliable. 

Artificial Intelligence in corporate finance 

One of the most valuable AI features is financial forecasting. For corporate finance, any changes in the market are vital and AI can predict most of them. The AI-powered tool provides deep analysis of historical data and current trends. It provides detailed research to enable accurate financial forecasts.  

It also helps to manage possible risks through market research and threat identification. AI fraud detection systems are often used in the corporate sector as well. The reason is simple: lowering the risk of financial crime makes the system more reliable overall. 

IBM Planning Analytics can vividly demonstrate AI’s efficiency in the corporate finance field. It is an AI-powered planning and analytics platform. Its main goal is helping companies with planning, budgeting, forecasting, and process analysis. Planning analytics helps with data-driven decisions based on accurate insights and scenario analysis. 

AI technologies used in finance

Artificial intelligence has enriched and transformed the finance industry with advanced capabilities. It covered such aspects as customer support, forecasting, fraud detection, and investment analysis. And all thanks to these AI technologies currently used in the finance industry: 

Machine learning 

ML models analyze large datasets to identify insightful patterns and make predictions about the existing system or workflow. This helps a lot in credit scoring, trading, and customer segmentation for target marketing. 

Natural language processing 

NLP is deeply involved in online customer support. Virtual assistants and chatbots can cover most of the common customer needs daily. NLP also analyzes public reactions to financial products and helps extract relevant financial news and insights. 

Robotic process automation 

RPA reduces human involvement in repetitive tasks, substituting them with AI functionality. AI grants automated data entry and validation processes. Predictive analytics models analyze historical and current data for future trend forecasting. This type of tool can predict various types of risks, including credit and security flaws. It can also anticipate future customer demand for financial products and services. 

Fraud detection and cybersecurity 

AI can easily detect anomalies and unusual behavior patterns, indicating possible fraudulent activities. It can set up round-the-clock transaction monitoring and control verification, identifying cyber threats and preventing potential intrusions into financial institutions. This allows for the dramatic rise of the infrastructure protection level. 

Personalized assistance 

Advanced tools provide financial advice and recommendations based on individual preferences and goals. Financial planning tools can help with budget organizing, savings, and retirement planning. 

Benefits of AI in finance 

Advanced technologies make AI a reliable partner and powerful assistant in financial aspects, granting the following capacities: 

• Automation of repetitive processes 
• Increased speed of transaction analysis 
• Advanced fraud detection 
• Risk reduction 
• Stable monitoring of anomalies 
• Pattern detection 
• Personalized approach to users 
• 24/7 interactions with customers 
• Minimized errors 
• Cost and time-saving assistance 
• More time for human personnel to focus on other tasks 

Challenges of artificial intelligence in finance

AI can change the finance industry for the better, but it also faces some challenges that you need to consider. 

Algorithmic errors 

Bias is a serious problem due to the systematic nature of such errors. AI model relies on data heavily. The quality and quantity can significantly impact its effectiveness. Algorithms learn from historical data. Biased or incorrect data may affect the whole system’s efficiency. Biased data can lead to inequalities, unfair outcomes, and improper investment offers. As a result, customer treatment can also be compromised.  

Using diverse and representative data for AI training will help to fix this issue. Implement data-cleaning processes to detect and mitigate biases in historical data, like ETL (Extract, Transform, Load). Advanced techniques can help detect corrupted data within the algorithms. Define and optimize fairness metrics in the development stage and training phase. Pay attention to post-development monitoring and feedback about errors. 

Security 

Security represents one of the most sensitive issues for the financial industry. AI systems can be vulnerable to cyber intrusions. Security breaches may seriously compromise financial data and market operations.  

To lower these risks, you may consider enhancing security measures through encryption and blockchain technologies. Ultimately, you need to develop a reliable infrastructure guided by ethics and AI strategy. It will help empower the protection system. You need to achieve safe data processing. Financial transactions also demand transparency and accuracy. Threat detection systems will also help identify potential risks and weak points. Data privacy is another vital point for the financial sector. 

Regulations and standards 

Compliance with standards and regulations is a base for trust in the AI model. Compliance grants stable and protected operations within the financial sector. Considering this point, you will be able to avoid most reputation and legal risks. Regular research will help you stay informed about any changes. Cooperation with proven experts, by the way, is the best way to achieve full-on compliance with regulatory requirements.  

It is better to save documentation of AI data usage, decision-making criteria, and development processes in common. You need users’ consent for data collection and usage. Don’t forget to provide clear information about how their data will be used.  

AI and human collaboration  

AI can simplify human work. However, it will not fully replace human operators. For successful integration, you need to reach effective collaboration between AI and the human team. It’s best to define the roles and responsibilities within the decision-making process. Decide where human judgment and verification are essential. Indentify where AI can act according to value-added insights. Make sure that the AI system is capable of explaining the decision transparently. 

Changing conditions 

The AI model is trained mostly on historical data. This means that changes in the financial market may cause lower effectiveness. The ability to adapt is crucial for success in unstable environments. Regular updates will serve well to fix this issue. It will guarantee that the AI receives the latest data and will know how to react to new circumstances. The monitoring and feedback collection will help control any issues with operation processes. Consider investments in scalable structure. It will be able to handle the growing size of datasets.  

DICEUS expertise with AI In the finance industry

DICEUS is always ready to improve your current systems with powerful, customizable AI solutions. We have worked with AI and RPA projects in various spheres, including finance, for years. 

For instance, we’ve implemented robotics process automation into payment processing to speed up the transfers. This solution reduces the need for manual human work and replaces it with automated procedures. It controls internal and external money transfers. The system also checks information about employees’ working hours. Optimized workflow allows the processing of all the payments within one day. Multi-system validation procedures help ensure the correctness of all data. 

In another project, we implemented robotics process automation for payroll processing. The client was interested in speeding up task fulfillment. The main requirement was to combine RPA tools with human validation. The notification system sends requests to human operators if anything goes wrong. Integrated NLP helps efficiently process various customer requests. 

Thanks to this implementation, 75% of all operations turned automated. This solution reduced the time required for payment decisions and decreased staff management expenses. 

Insurance is one of the key parts of the financial industry, and we have expertise in this sector as well. DICEUS developed a Vitaminise LLM-based chatbot for the insurance industry. This bot scans and reads the text from the documents uploaded by policyholders. The required data from the document is extracted into a readable format. This AI-powered tool simplified claim submission for users, whereas insurance companies get all the required in a flash.  

Bottom line

Artificial intelligence is a key to the finance industry’s evolution. It covers areas from decision-making and operation efficiency to risk management. AI comes with significant advantages and serious challenges. You will need to achieve data privacy, improved security, and an error-free environment. AI implementation is a complex task that you can solve with a strategic approach and an expert team. Our professional team of developers will be glad to tailor and train AI models to your needs. 

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