Dorota Jasińska
Marcin Dobosz
The use of AI in the fintech sector is steadily growing and transforming finance services. It’s implemented in many fields, including customer service, cybersecurity, and data processing and analysis. As financial service providers deal with vast amounts of personal and financial data, it’s natural they look for innovation. AI brings what is so much needed in fintech. Thanks to AI, financial institutions are capable of categorizing transactions quickly and efficiently while minimizing errors and much more.
AI influencing financial services
Financial institutions must process and analyze the data quickly and accurately to provide customers with quality real-time services. They face many challenges in the process, such as managing and categorizing transactions, dealing with complex data, etc. Effective transaction categorization methods require specific solutions. Financial transactions have different contexts, that’s why it’s so hard to manage them with traditional methods.
As AI is used to automatize various financial processes, it’s also implemented in the case of transactions and their categorization. Transaction categorization is a process of assigning transactions to specific categories. Based on its data and description, each transaction gets an appropriate category. This allows for further analysis of transactions. Now, categorization is handled by algorithms and AI.
AI transaction categorization is great for financial institutions and customers. It helps overview personal spending, contributes to better customer service, supports fraud detection, and automates processes.
Customer financial management
Attributing transactions to a specific category helps learn the spending tendencies of a user. Once categorized, transactions show how the funds are used by a customer, and this allows for improved budget control and an overview of one’s financial health. The customer knows if they spend too much on leisure and can decide on how to deal with such a situation. In the case the transaction is well-categorized, the customer may raise fewer questions about their origin to customer service.
Personalized services for customers
As soon as the financial institution notices some irregularities in customer spending habits, they can reach out with a custom solution or offer to cater to the client’s needs. Financial service providers can offer personalized advice and tailored services or recommend a specific product depending on the requirements.
Boosted fraud detection
The AI-powered categorization makes it possible to identify unusual transactions or spending patterns. This way, banks and other financial institutions can detect irregularities and fraudulent activities. This also improves the anti-money laundering (AML) strategies and know-your-customer (KYC) regulations, influencing regulatory compliance.
Decreased chargeback rate
As AI can quickly and accurately categorize transactions, their descriptions are clearer to customers. This reduces the risk of unnecessary chargeback requests on transactions customers can’t recognize. Such transaction optimization can decrease
erroneous chargeback reports and may lead to customer trust loss and potential regulatory penalties. Neontri’s solution, with the use of AI and data enrichment, adds layers of information to each card transaction. This includes store names, product categories, prices, and dates, which help identify transactions.
The technology behind the categorization
What technologies are used for transaction categorization? The base of AI-powered categorization is machine learning. AI also uses NLP processes, deep learning, and other techniques to improve its algorithms. This allows for efficient processing of the data to categorize transactions correctly. AI transaction categorization combines machine learning algorithms, NLP, and data analysis.
The process starts with collecting transaction data. Transaction message formats were standardized by ISO to simplify global financial communication. In this case, ISO 8583 is used for card transactions, and ISO 20022 is the universal standard. These help categorize certain transactions. AI transaction categorization is a complex process, so let’s look at how it works step by step.
Data gathering and processing
The process begins with data collection, and then the algorithms can process them into a structured format that AI models understand and learn from. The collection of transaction data comes from different sources, such as banks, payment processors, or financial apps. The transactions include details like transaction amount, date, and transaction description.
Then, the transaction data is cleaned. Some transactions may have missing information. Algorithms convert the preprocessed data into a normalized format. The transaction descriptions are crucial for determining its category. In the case of textual data, the descriptions are split into individual words or tokens. AI changes the data into numerical vectors that enable the AI models to learn from it.
The process involves adding specific information to the transaction data that fills up the missing ones to make identification easier. It’s called data enrichment and makes it possible to fill in the information to the raw transaction data that are required to improve the accuracy and consistency of transaction categorization.
Model training and implementation
In classic Machine Learning, AI gets the data to learn from, so that the models can train on it to categorize transactions. The data is split into a few sets that need to be labeled for a classic ML to work: training, validation, and testing. The labeling can be done manually by assigning the category to a transaction (training set). The model is trained on the training set that should involve examples of transactions with proper categories. Models can learn from historical transaction data that was manually categorized. This allows it to learn patterns for specific transaction categories.
Then, the model’s performance is validated and evaluated. The performance is checked, and the model is improved if needed. When the model performs well, it can be implemented.
Impact on data analysis
Implementing AI in transaction categorization helps automate the categorization process, present transaction data in a clear way, and reduce operational costs, just to name a few. There are many areas that can benefit from AI, including data analysis.
Enhanced accuracy and efficiency
AI automation speeds up the categorization process. It’s capable of processing huge amounts of data in real-time while delivering insights on spending patterns, fraud threats, etc. The algorithms help automate repetitive tasks, such as transaction categorization, so that people can focus on more complex jobs. Moreover, AI’s scalability is a huge advantage in terms of analyzing increasing data volumes. All this results in more accurate financial analysis and improved reporting.
Data-driven insights
Automated data processing with proper analysis provides financial institutions with a better understanding of customer behavior and their financial health. Such insights can influence product development and customer engagement strategies. Accurate transaction categorization showcases customer financial flows so financial institutions can get a clear picture of spending patterns. Such insights allow for the customization and personalization of financial services, such as personalized advice and financial products.
Predictive analytics
AI can also boost the understanding of customer needs thanks to predictive analytics. After analyzing customer financial behavior and spending patterns, the algorithms can forecast future spending trends or product needs. It also allows observing any changes in the cash flow and reporting potential perspectives for loans. AI and predictive analytics can help make strategic decisions and anticipate probable customer needs in terms of financial services.
The challenges of AI transaction categorization
Implementing AI in fintech also brings about some challenges. Keeping the data secure and the models operating on quality training data are only a few of them.
Data privacy and security
One of the challenges is keeping the data private and secure. All transaction data is sensitive and must be protected. Financial institutions are required to comply with regulations such as GDPR and CCPA to ensure proper security. The data should be encrypted to prevent unauthorized access. It’s essential to conduct regular security audits. This helps identify potential threats and security risks.
Need for quality data
For the AI models to operate on the highest levels, they must be provided with new data. Regular updates are essential to avoid errors and adapt to changing categorization processes. The dataset for training must be enriched with the latest concepts, trends, and changes in types of transactions. Models should incorporate new knowledge while keeping information learned from older data.
AI transaction categorization solutions
AI and ML have significantly improved the process of transaction categorization. The technology responsible for categorizing transactions is becoming more accessible. There are a few options on the market that can be implemented to improve the categorization process.
Depending on the business needs, providers can help with the AI transaction categorization process. Some offer APIs capable of enriching transactions with information customers lack in bank descriptions. The data enrichment usually adds information about the merchant, logo, and website.
Digital transformation with AI
Artificial intelligence has strongly influenced the fintech industry. Looking at its possibilities, the industry can benefit from implementing it into the services. AI-powered transaction categorization is one of the options for financial institutions to embrace – innovation. AI’s capability to enhance the accuracy and efficiency of data analysis is one of the key benefits. It can also help with data-driven decisions, predictive analytics, and more.
