In financial services, unstructured data comprises over 80% of the data generated, including documents like contracts, investment analyses, trade records, and customer interactions. This data holds critical insights but remains largely unused due to its complexity. Financial organizations have traditionally relied on structured data, as it’s easier to process and analyze. However, limiting automation to structured data overlooks valuable information in unstructured data that can enhance customer insights, risk assessment, and regulatory compliance. This also includes a company’s institutional knowledge that is generally left on the table or requires a manual process of having an employee or subject matter expert pass down the knowledge from one individual to the next.

1) Enhancing Investment Decision Support

Investment decisions often rely on diverse data sources, many of which are unstructured. Analysts sift through research reports, earnings calls, client communications, and news articles—all rich in qualitative insights but challenging to process at scale. By leveraging large language models, wealth management firms can analyze unstructured datasets to extract relevant insights, improving investment recommendations and portfolio management.

For instance, AI provides companies with the ability to identify sentiment and extract key financial metrics from company earnings reports and market news, which can be integrated into analysts’ decision-making processes. This allows financial professionals to uncover trends and opportunities more quickly, providing a competitive edge in a fast-paced industry.

2) Automating Compliance and Regulatory Reporting

The financial services industry faces stringent regulatory requirements, from Anti-Money Laundering (AML) and Know Your Customer (KYC) to data privacy and risk reporting. Much of the compliance-related information, including email communications, transaction records, and audit documents, is unstructured and requires extensive manual review.

By leveraging an AI platform that can process unstructured data, financial institutions can streamline compliance workflows, reduce manual labor, and improve accuracy. Take fraud detection, for example: AI can automatically flag unusual activities or suspicious language in customer interactions, supporting Anti-Money Laundering (AML) and fraud prevention efforts. It can also streamline regulatory reporting by organizing and structuring necessary information, ensuring timely, accurate submissions and reducing compliance risks.

3) Improving Customer Service and Personalization

As digital banking and personalized wealth management continues to gain popularity, customer expectations for tailored services are higher than ever. Unstructured data from customer feedback, support emails, social media, and service interactions provides a valuable source of insights into client preferences and concerns. Today, this is a manual process that requires a substantial amount of overhead to sift through and even more if you want to integrate it into your existing workflows.

With a proper AI platform, you can analyze this unstructured data to identify patterns and sentiments - helping financial institutions deliver personalized recommendations and proactive service. For example, businesses could use AI to detect dissatisfaction in customer messages and trigger automated outreach from a representative. This would drastically improve customer satisfaction and retention. By incorporating insights from unstructured data into customer profiles, financial services can deliver more personalized and responsive service, differentiating themselves in a competitive market.

4) Enhancing Risk Management and Fraud Detection

Unstructured data plays a critical role in identifying risks and preventing fraud in financial services. Regardless of what sector of financial services you’re in, data from transaction logs, email communications, trade documents, and external sources can help institutions detect patterns of fraudulent behavior or emerging risk factors.

With today’s advancement in technology, AI-powered tools can be used to analyze unstructured data for unusual patterns, keywords, or anomalies that may indicate fraud or high-risk transactions. For instance, algorithms can evaluate email content and external news reports to assess potential risks related to specific clients or investments. By automating the analysis of unstructured data, financial firms can proactively manage risks, reduce the incidence of fraud, and maintain more robust security measures. The real challenge however is finding a platform that can efficiently process unstructured data to automate complex workflows.

5) Streamlining Research and Financial Analysis

Investment firms, banks, and insurance companies rely heavily on research and analysis to make informed decisions. This involves sifting through large volumes of unstructured data, including financial reports, news, economic forecasts, and proprietary research.

With AI, financial analysts can automate the extraction and synthesis of information from unstructured data sources. For instance, AI tools can scan news and research reports for relevant insights, consolidating information that may impact investment strategies or product development. By streamlining research efforts, financial institutions can improve analysis efficiency, reduce time to insight, and drive better investment and business decisions.

6) Ensuring Data Security and Privacy Compliance

Financial services organizations are custodians of sensitive customer data and are required to adhere to strict privacy and security regulations, such as the General Data Protection Regulation (GDPR), SOC 2, and various national laws. This responsibility extends to unstructured data, such as emails, documents, and chat logs, which often contain PII.

AI platforms can help identify, classify, and secure unstructured data, ensuring compliance with data protection regulations. These tools can automatically detect sensitive information in unstructured datasets, enabling organizations to enforce data governance policies, manage data access, and secure client information. With robust data security and privacy measures, financial institutions can build trust with clients and regulatory bodies alike.

Unstructured data holds immense potential for financial services. By leveraging advanced AI and automation, firms can unlock insights that drive efficiency, enhance decision-making, and deliver personalized experiences in an increasingly data-driven landscape.