The challenge of text summarization in financial services
The financial services industry generates an immense volume of documentation daily. From customer interactions and regulatory filings to legal proceedings and risk assessments, organizations must process, interpret, and act upon large amounts of unstructured data. Traditionally, this has been a time-consuming and labor-intensive process, often susceptible to human error and inconsistencies. As regulatory frameworks evolve and customer expectations rise, the demand for accurate, efficient, and standardized document summarization has never been more critical.
In banking, institutions must navigate a constantly shifting regulatory landscape. Compliance teams are responsible for reviewing extensive regulatory filings, risk reports, and audit documents—any misinterpretation can result in significant financial and legal consequences. Beyond compliance, customer service operations require rapid access to key insights from call center interactions to enhance service efficiency. Additionally, loan and credit risk assessment teams manually analyze financial statements, credit histories, and other documents to determine creditworthiness, a process that is both time-intensive and costly.
The insurance sector faces similar challenges, particularly in underwriting, policy management, and claims processing. Insurance providers must constantly interpret complex regulatory changes while ensuring accurate policy underwriting and risk assessment. Claims processing teams review medical reports, legal documents, and third-party assessments to determine coverage and fraud risk. Manual document reviews in these areas not only slow down operations but also introduce inconsistencies that can impact decision-making.
The increasing complexity of financial services documentation makes manual summarization an unsustainable approach. Generative AI (GenAI) offers a powerful solution by enabling automated summarization of key insights from various documents. However, assessing the quality of AI-generated summaries remains a challenge. Traditional evaluation methods, such as ROUGE and BERTScore, rely on human-generated references, which are not always available or practical for large-scale financial services applications.
Introducing QAG-based automated summarization evaluation
Question-Answer Generation (QAG) for automated summarization evaluation provides a breakthrough, offering a reference-free approach to ensuring both completeness and accuracy in AI-generated summaries. Instead of comparing summaries to predefined references, QAG-based evaluation gauges summarization quality by generating factual questions from the original document and checking whether the AI-generated summary provides correct answers.
Experimental results
Optimization techniques for QAG were implemented that included limiting truth extraction and using custom question templates to improve evaluation performance.
This enhanced QAG-based evaluation approach was then tested on four real-world transcripts. In each test, both the default QAG model and our optimized approach were implemented. The following table summarizes the results:
Overall, the experimental results reveal a significant leap in alignment scores, rising from a baseline of 56% to over 70%, while coverage scores experienced an even greater boost, increasing from 70% to 90%. These enhancements demonstrate the effectiveness of the refined approach in producing more accurate and comprehensive AI-generated summaries.
Wide-ranging use cases in banking and insurance
By implementing QAG-based evaluation, financial institutions can improve the reliability and accuracy of GenAI-powered summarization across multiple business functions. In banking, it ensures that compliance reports, customer interactions, and financial risk assessments maintain factual integrity. In insurance, it enhances underwriting decisions, policy management, and claim evaluations. The following is a sample of several key use cases in financial services.
Banking use cases
- Call center interaction summarization: Customer service teams manage a high volume of customer interactions, often recorded in call center transcripts, chat logs, and emails. GenAI can summarize these conversations, extracting key themes, customer concerns, and sentiment trends, enabling more efficient issue resolution. With QAG-based evaluation, AI-generated summaries ensure that no critical customer concerns are overlooked, allowing for more personalized and proactive customer support.
- Audit report summarization: Internal audits are a critical part of risk management in banking, yet the process is often time-consuming and labor-intensive. AI-powered summarization helps highlight key discrepancies, compliance violations, and recommended actions from audit reports, improving the efficiency of risk and compliance teams. With QAG-based evaluation, banks can ensure that summarized audit findings remain aligned with the original reports, reducing the chances of oversight in risk assessments.
- Credit risk assessment: Evaluating a borrower’s financial health requires the review of credit reports, financial statements, and loan histories, often spread across multiple documents. GenAI can consolidate key financial indicators into a structured summary, allowing risk analysts to make faster and more informed lending decisions. By applying QAG-based evaluation, banks can verify that these summaries accurately reflect the borrower’s financial status, reducing errors in credit risk assessments.
Insurance use cases
- Underwriting and risk assessment: Insurance underwriting requires the evaluation of extensive data, including health records, financial documents, and previous policy claims. GenAI-generated summaries allow underwriters to quickly assess risk factors, policy eligibility, and pricing considerations. With QAG-based evaluation, insurers can confirm that these summaries capture the full scope of risk assessment criteria, reducing underwriting errors and improving decision-making efficiency.
- Policy management: Managing policies involves handling a large amount of unstructured documentation throughout the policy lifecycle. Any modifications initiated by insurers or customers require careful reassessment. GenAI streamlines this process by efficiently condensing information from various sources. By applying QAG-based evaluation, insurers can confirm that AI-generated summaries align with policy terms and regulatory requirements, enabling them to allocate more time to strategic tasks such as customer service and relationship management.
- Claims processing: Whether for auto, healthcare, or commercial policies, claims processing is a complex, documentation-heavy task that demands significant time and effort when done manually. GenAI automates the extraction of critical details from diverse records. QAG-based evaluation ensures that all necessary claim details are preserved, reducing operational costs, expediting claim settlements, and improving overall customer satisfaction.
These use cases highlight just a few of the many ways QAG-based evaluation can be applied in financial services. Potential applications extend far beyond these examples. Depending on an organization’s specific needs, QAG-based evaluation can be adapted to review AI-generated summaries across a wide range of business functions, including regulatory reporting, contract analysis, investment research, internal policy compliance, and more.
Driving accuracy, efficiency, and trust in AI-generated summarization
As financial institutions increasingly rely on GenAI to streamline document processing, ensuring the accuracy and reliability of AI-generated summaries is paramount. QAG-based automated summarization evaluation provides a reference-free, scalable, and precise method to assess summarization quality, addressing one of the key challenges in AI adoption. By evaluating summaries based on factual correctness and content coverage, QAG-based evaluation offers a structured approach to verifying AI outputs without the need for human-generated reference summaries.
The benefits of integrating this approach in banking and insurance are far-reaching. Banks can enhance decision-making by quickly extracting key insights from financial reports, compliance documents, and customer interactions. This leads to faster responses to regulatory changes, improved operational efficiency, and a more seamless customer experience. In the insurance sector, QAG-based evaluation improves underwriting accuracy and claims processing efficiency, ensuring that AI-generated summaries are both comprehensive and aligned with business objectives.
Now is the time for financial institutions to embrace AI-powered summarization with QAG-based evaluation. To explore how this approach can elevate your organization’s AI-driven summarization efforts, contact Capgemini’s Financial Services Insights & Data team today.
