Financial services firms are burdened with a significant challenge in managing their data, given that a large portion of this information typically exists in unstructured formats. Data extraction is often a manual process – one that is not only time-consuming but also prone to errors.

This makes it a painstaking task to process large volumes of financial data effectively and efficiently.

For example, according to research from InterSystems, an overwhelming 81% of financial technology firms identify data issues as their biggest technical challenge. Of this figure, 41% struggle to leverage data for analytics, machine learning, and artificial intelligence, while 40% are unable to connect to customers’ applications and data or legacy systems.

Here, we'll explore how groundbreaking technologies like artificial intelligence, machine learning, and natural language processing can help financial firms automate data management tasks.

Unleashing the Power of AI and ML

Artificial Intelligence (AI) and Machine Learning (ML) are powerful technologies that have applications in a variety of business practices, from data analysis to customer engagement and even product development. AI is capable of processing colossal amounts of data at a rapid pace, far beyond human capabilities. It can sift through structured and unstructured data, identify relevant parts, and condense the information into manageable chunks.

Machine Learning, a subset of AI, is a technology that enables systems to learn from past data and predict future outcomes. This predictive capability is particularly useful in financial services, where ML models can forecast market trends, assess risks, and optimize investment strategies.

Examples of AI in Action

Many financial firms have already deployed applications of AI and ML through the use of proprietary software and turnkey solutions.

However, new applications of AI are being realized all the time. According to The Motley Fool, "A number of startups are working AI fraud detection programs, and IBM has an AI program under IBM Watson Studio that improves fraud detection, fraud prediction, and fraud prevention."

Similarly, insurance provider Lemonade uses AI in customer service interactions, providing customers with offers for quotes. The company also uses AI to process claims. According to the report, "AI-Jim," the company's AI claims processing agent, paid a theft claim in just three seconds.

The company says it settles close to half of its claims now using AI technology.

Finally, AI Magazine reports that SAP, a leading technology company, is using AI in its ERP system to significantly improve finance processes. Specifically, its SAP Cash Application uses AI to automate the manual task of matching open receivables with incoming bank statement items, supplier-initiated payments, and more.

Overall, the solutions streamline the financial closing process by identifying and resolving discrepancies between intercompany transactions.

Role of Robotic Process Automation

Robotic Process Automation (RPA) further complements these capabilities. RPA is a technology that automates repetitive tasks, freeing human resources to focus on more strategic and value-adding tasks. In many ways, the generative power of AI would not be possible without RPA.

By automating data extraction and processing tasks, RPA eliminates the possibility of human errors, thereby enhancing data accuracy.

For example, a financial services firm could use RPA to automatically extract information from thousands of documents, analyze the data, and create reports. This could significantly reduce the amount of manual labor required for generating insights.

Natural Language Processing

NLP, on the other hand, is a field of AI that gives machines the ability to understand and interpret human language. It is already being used widely in call centers, enabling customers to navigate menus simply by speaking into their phones.

However, NLP can also be leveraged to understand and analyze unstructured financial data such as reports, records, and documents, transforming them into structured data that can be processed more efficiently. According to Forbes, "NLP models like ChatGPT can be used to extract insights from unstructured data, such as customer reviews or social media posts, which can provide valuable insights into customer sentiment and needs."

This type of capability holds immense promise for financial firms and their ability to better service customers. Using NLP, they could analyze customer conversations and offer personalized advice that meets the individual needs of each client.

Advantages of Automating Finance Data Management

The automation of finance data management using AI, ML, RPA, and NLP can lead to several benefits. With the ability to process vast amounts of data quickly and accurately, these technologies can help uncover hidden patterns, trends, and insights that drive better decision-making.

They also reduce the need for manual intervention, saving time and resources while minimizing errors.

In conclusion, the use of AI and ML in financial data management is not just about automation, but about augmenting human capabilities. As the financial world becomes increasingly data-driven, the firms that will thrive are those that can effectively leverage these technologies to turn their data into actionable insights.

To learn more about how your company can leverage these technologies for data management, don’t miss FIMA Connect US 2023. It’s happening from December 6^th through December 8^th at the Hilton West Palm Beach in West Palm Beach, Florida.

Download the agenda and request an invitation for the event today.