Yet, the real transformative capability of RPA in insurance comes with the addition of artificial intelligence (AI). With AI, RPA evolves into an adaptive tool that can handle unstructured data, make context-based decisions, and learn from experience. These capabilities are particularly beneficial in the insurance industry, where data processing, accuracy, and timely decision-making are integral to business efficiency.
In this article, we’ll explore how AI-enabled RPA in insurance is revolutionizing the industry with three top use cases: bordereaux reconciliation, submission intake, and claims management. We’ll also take a look at how Brisc’s AI platform is empowering insurance teams with the capabilities they need to work smarter, grow faster, and reduce costs.
AI-powered RPA in insurance leverages technologies like Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision to improve automation processes. With the combination of AI and RPA, insurers and MGAs can tackle complex workflows, automate decisions, and streamline data-intensive processes more effectively. The combination enables organizations to:
One of the most significant pain points that insurers and MGAs have with bordereaux processing and cash reconciliation is that bordereaux are typically received in various formats. Standardization and validation of bordereaux data is time-consuming and error-prone, and often lead to delayed financial closing, inaccurate reporting, and compliance issues.
AI-powered RPA can easily automate bordereaux data ingestion, validation, and reconciliation, enabling finance and operations personnel to focus on analysis and exception management.
So how does it all work? AI-Powered RPA in insurance uses Intelligent Document Processing (IDP) to extract data from various formats and automatically validates this data against rules and historical data. It then identifies discrepancies, reconciles data, and formats data for ingestion into core systems and reporting.
Use of AI-powered RPA in insurance results in faster bordereaux processing and cash reconciliation cycles, drastically reducing manual effort and errors in financial processes.
Brisc's Bordereaux Reconciliation & CashOps Agent normalizes diversified data types, verifies information, and reconciles transactions more accurately than ever before using AI. Leveraging the capability of AI-driven data extraction, Brisc can extract information from PDFs, Excel files, emails, and more automatically, thus eliminating errors. The Brisc platform employs machine learning algorithms to identify irregularities, flag potential issues, and provide full traceability for auditing purposes.
Brisc's reconciliation engine ensures 97%+ accuracy as well by learning from past reconciliation processes, utilizing pre-defined rules automatically, and highlighting exceptions that require human intervention. The AI-powered solution reduces reconciliation cycles by 10x, diminishes revenue leakage, and provides real-time visibility into cash flow, enhancing overall financial visibility and operational efficiency.
Underwriters are often overwhelmed by the sheer volume of submissions received. And it’s not just volume that’s the issue– these submissions are in various formats (emails, PDFS, documents). Unfortunately, the manual process of sifting through these submissions and pulling, categorizing, and prioritizing data in multiple formats has led to bottlenecks, delayed quote times, lost business, and underwriters spending time on administrative tasks.
AI-powered RPA converts submission intake into an intelligent gatekeeper that automates processing incoming submissions from various channels. As noted above, it leverages Intelligent IDP and advanced AI algorithms for extracting and structuring data from varied formats, converting unstructured data like handwritten notes, scanned paper, and poorly formatted files.
But it does more than data extraction. AI-powered data validation rules, missing data identification, and risk factor assessment are all executed by AI algorithms using historical trends and underwriting guidelines. Moreover, they can be designed to smartly prioritize submissions based on metrics such as potential risk exposure, expected premiums, or time sensitivity so that underwriters can work on high-priority opportunities first.
This advanced triaging streamlines quote turnaround times and enhances overall underwriting precision and consistency, enabling insurers to respond more to market opportunities while minimizing risk exposure.
Brisc Intelligent Submissions Agent transforms submission handling by automating data extraction, validation, and prioritization. The agent processes data from numerous sources quickly and with pinpoint precision.
Brisc's response goes beyond mere automation. It uses AI to transform unstructured data into standardized forms, such as schedule values (SOVs). This reduces errors and maximizes data accuracy, allowing underwriters to make more confident decisions.
One of Brisc's biggest strengths is its ability to strategically prioritize valuable submissions. The system highlights the majority of the most profitable opportunities by utilizing AI algorithms that examine potential risk exposure, projected premiums, and other relevant indicators. Such prioritization helps underwriters target their efforts and achieve the greatest opportunity for writing valuable business.
Processing claims is notably data-intensive and time-consuming. It requires the processing of a great range of documents, policy checks, and intricate decisions all while striving for high accuracy and preventing fraud. Such activities are likely to be subject to delays, manual data entry errors, and inconsistency when performed manually.
AI-based RPA revolutionizes claims management by integrating cognitive AI capabilities and automation efficiency. Instead of depending on manual keying and validation, AI-based RPA can extract data from claim forms automatically, retrieve pertinent information from supporting documents (repair estimates, medical reports, invoices) and validate against policy conditions to ascertain coverage. Processing time is significantly reduced, and human error possibilities are eliminated.
In addition, artificial intelligence algorithms assist in fraud detection by analyzing claims data for suspicious patterns, such as overcharged repair rates or incoherent medical reports. Machine learning algorithms can detect possible fraudulent claims for further scrutiny to prevent insurers from suffering financial losses and improve overall risk management.
AI also streamlines claims triage through the intelligent sorting of cases by size, complexity, and urgency. RPA can process simple payouts and communications automatically for less complex claims so that easy cases are closed more quickly without the intervention of adjusters. In more complicated cases, AI systems can provide underwriters and adjusters with valuable insights from historical data to improve decision-making.
Brisc's Claims Management Agent automates data extraction, validation, and triage with AI-driven Intelligent Document Processing (IDP) to capture data from claims forms and supporting documents. It utilizes machine learning algorithms to detect suspicious patterns and potential fraud via inconsistencies between claims data and images.
Its AI-powered triage system prioritizes claims by claim size, urgency, and complexity so that straightforward claims are resolved with automated payout and communication. At the same time, complex cases receive detailed insights for adjusters. The end-to-end automation reduces processing times by up to 50%.
While the three uses discussed are big areas of focus, AI-led RPA will likely be used across the insurance industry's value chain. Policy administration is very important, and AI-led RPA can automate the end-to-end policy life cycle, from quote generation to policy issuance to renewals and endorsements. These laborious processes are taken care of in an automated fashion, reducing the operational overhead and ensuring uniform data across multiple systems.
Another area ready to be transformed with AI-powered RPA is customer service. Insurers can automate repetitive customer queries, policy renewals, and requests for claim status without human interaction using AI-driven chatbots and automated email responses. This accelerates response times and enables customer service representatives to concentrate on more valuable issues.
In reporting and regulatory requirements, RPA driven by AI can ensure compliance accuracy in submitting regulations, generating audit trails, and reconciling data. AI algorithms can map transactional information to compliance checklists, eliminating errors and the risk of penalties.
AI-facilitated RPA can also improve financial processes like accounts receivable/payable and financial forecasting. Financial reporting automation, cash flow analysis automation, and invoice processing will allow insurers to gain real-time insights into their financial health, mitigate risk, and make sound decisions.
By leveraging AI-powered RPA, insurers can automate complex, data-heavy operations with unmatched speed and precision. Brisc’s automation solution set, including agents for bordereaux management, submission intake, and claims handling, allows insurers to streamline operations, reduce costs, and mitigate risks. With AI-driven insight and automation, Brisc empowers insurers to maximize operational efficiency while maintaining accuracy and compliance.
Ready to accelerate your insurance operations with AI-powered RPA? Book a demo to see Brisc in action and discover how our intelligent automation solutions can enable you to stay ahead of the competition.