Artificial Intelligence (AI) has become a game-changer for the financial services industry. The use of AI in finance can help companies make better decisions, optimize operations and improve customer experience. In recent years, AI has been applied to various financial services, such as fraud detection, risk management, investment analysis and customer service.
One of the key benefits of using AI in finance is its ability to process and analyze large amounts of data at high speed, making it possible to identify patterns, trends and anomalies that might go unnoticed by human analysts. AI-powered tools can help financial institutions streamline operations, reduce costs and increase efficiency, ultimately leading to better financial performance. In addition, AI can help financial services firms personalize their offerings to individual customers, providing a more tailored and relevant experience that drives customer loyalty and satisfaction.
In this blog post, our experts share their experiences of working on challenging AI projects in the financial services industry.
Automated Data Anonymization
For financial services companies, there are very strict regulations around data anonymization and privacy. GDPR requires data to be anonymized or pseudonymized before it is processed or stored. Potential data breaches, such as those experienced by Capital One or First American Financial Corporation, would have increasingly severe consequences if data is not anonymized. Data applications such as data sharing or data analytics, which can be a major source of revenue, are not possible without properly anonymized data.
While anonymization is a no-brainer for companies in the financial services industry, the manual process is often cumbersome and error-prone due to the sheer volume of data. However, Natural Language Processing (NLP) applications can automate the data anonymization process.
Data can be structured or unstructured. Structured data can be tabular data, such as customer transactions, where each field of a transaction is well-defined. In this setting, a person or model responsible for anonymization only needs to identify the fields that need to be anonymized or pseudonymized. For large amounts of data, this is often not trivial, and assistance from a language model can be very beneficial.
On the other hand, unstructured data, such as customer inquiries, cannot be easily anonymized. The data does not follow clear patterns and can contain any information at any point. For example, people’s names, phone numbers, or email addresses can be a good starting point for the anonymization process. In the technical world, the process of identifying specific terms in text is often referred to as part-of-speech (POS) tagging.
Our project experience shows that even with existing NLP models it is possible to generate a high degree of automation in data anonymization. Without a clear model understanding and validation process, it is recommended that the anonymization results are checked by domain experts. However, one of our core competencies is model certification and validation, which allows us to almost completely automate this process.
Fraud is a primary concern for financial services organizations because it can result in significant financial loss, reputational damage, and legal and regulatory consequences.
Financial fraud can take many forms, including credit card fraud, identity theft, money laundering, and embezzlement. Fraudulent activity can cause significant financial loss to financial services companies and their customers. These losses can result from unauthorized transactions, fraudulent loans or investments, and other forms of financial misconduct.
As a result, fraud detection is an important topic on the agenda of many organizations in the financial services industry. While manual and statistical methods already exist and produce meaningful results, the application of AI and machine learning has the potential to significantly improve the fraud detection process by significantly improving the accuracy and speed of fraud detection while reducing false positives.
Algorithms can analyze vast amounts of data, including transactional data, user behavior patterns, and other relevant information, to identify potentially fraudulent activity. By analyzing patterns in the data, models can identify anomalies that may indicate fraudulent behavior, such as unusual transaction amounts, unusual transaction timeframes, and irregular transaction locations.
Using AI for fraud detection also helps organizations detect fraud in real-time, which can prevent fraudulent transactions from being processed and minimize financial losses. In addition, AI can learn from past fraud cases to identify new types of fraud and adapt its algorithms to detect new threats.
In addition, AI-powered fraud detection systems can reduce the amount of time and resources spent manually investigating potentially fraudulent transactions. This can help financial services firms improve operational efficiency, reduce costs, and better allocate resources to other important tasks.
Intelligent Portfolio Management
Portfolio management is a complex process that requires extensive research, analysis, and monitoring to ensure that clients’ portfolios are aligned with their investment goals and risk tolerance and perform well over time.
Recommender systems are a perfect AI technology for portfolio management because these systems can provide personalized investment advice to clients based on their investment goals, risk tolerance, and past investment behavior. These systems can analyze large amounts of data, including historical market data and individual investment histories, to provide clients with customized investment recommendations.
They can also help financial services firms manage portfolios more efficiently by automating many of the routine tasks involved in portfolio management, such as rebalancing portfolios and identifying investment opportunities. This can save time and resources, allowing firms to focus on providing high-quality, personalized service to their clients.
In addition, such systems can help financial services firms build trust and loyalty with their clients by providing them with transparent, data-driven investment advice. Customers are more likely to trust a firm that can demonstrate the rationale behind its investment recommendations and provide evidence of its track record.
Overall, the use of recommender systems can improve the efficiency, effectiveness and transparency of portfolio management for financial services firms, while providing clients with personalized investment advice that can help them achieve their financial goals.
In this article, we discuss three different areas where AI technology can be applied in the financial services industry.
The first is automated data anonymization, where natural language processing (NLP) can be used to automate the process of anonymizing or pseudonymizing data, which is required by GDPR regulations.
The second area is fraud detection, where AI and machine learning algorithms can analyze large amounts of data to detect potentially fraudulent activity and improve the accuracy and speed of fraud detection.
The third area is intelligent portfolio management, where recommender systems can provide personalized investment advice to customers based on their investment goals, risk tolerance, and past investment behavior, automate routine portfolio management tasks, and provide transparency and data-driven investment advice.
Overall, the use of AI technology in these areas can improve the efficiency, effectiveness, and transparency of the financial services industry. This also applies to other applications such as customer service, process control, or risk assessment.
Manuel Lang is a Machine Learning Engineer with a deep passion in automation. He has a background in Computer Science with a specialization on Machine Learning, Robotics and Automation and has applied his knowledge of over 10 years of programming experience to projects ranging from small startups to big corporates like NASA or political institutions like the German Ministry for Labor and Social Affairs. Prior to working in industry, he also enjoyed his research in Machine Learning Theory at Carnegie Mellon University where he invented and published work on novel algorithms in Unsupervised Machine Learning.