Hello everyone. Welcome you all to this session, generative AI Transforming Healthcare Claims Processing. I'm Amala Ala Oma. I work as a junior principal data engineer at a management consulting firm. I partner with our clients in leading data architecture and digital transformations by building scalable and robust data pipelines at an enterprise scale. It's a pleasure to be here at Con 42 Q, native 2025. Today I'll be sharing my views on how gene AI is transforming healthcare claims processing space, especially in speeding up operations, improving accuracy and detecting frauds in ways traditional systems simply cannot. Let's dive into the challenge first. The healthcare claims ecosystem is massive. Tens of millions of claims are submitted, reviewed, and processed. Every single day across several payers, providers, and insurance companies. Unfortunately, the system is so fragmented, slow, and highly error prone due to the fact that how these systems are built and managed claims essentially move through multiple layers of processing, like eligibility, coding, validation, adjudication, and audits. Each step introduces potential for errors or delays. According to recent studies, nearly 15% of claims are either denied or delayed because of various reasons like incorrect data, missing documentation, or inconsistent codes, and then this fraud ranging from upcoding to phantom billing to collusive networks of providers. These issues cost the healthcare industry over a hundred billion dollars annually. Traditional rule-based systems cannot adapt fast enough to detect evolving patterns. That's where gene AI comes into picture. Gene, I introduces a paradigm shift almost in every industry. The healthcare industry is no exception. Unlike fixed rule automation, gene, I systems learn from vast and diverse, unstructured and semi-structured data. Like claims text, EHR, records, image, billing nodes, et cetera. It also generates context aware and explainable insights. Let's break it down to see what makes it transformative in top three aspects. Number one, the speed AI agents can process and validate claims in seconds nowadays, it streamlines the most time consuming manual process like data entry, verification, and prior authorization. This reduces the administrative burden on providers and expedites the payments in one case. Generic model, increased automated claim processing by 30%, resulting in faster approvals. Number two, accuracy. General leverages natural language processing and contextual understanding to reduce coding errors and mismatch entries. This ensures coding, accuracy and completeness before a claim is submitted. This minimizes human error resulting in higher first pass claim rate and fewer denials. And third, the proactive fraud detection. AI analyzes large data sets. To correctly identify the subtle and suspicious billing patterns or anomalies in the data that even humans or rule engines often miss in traditional road based systems By flagging questionable claims in real time, genea helps insurers prevent financial losses and strengthen system integrity. So instead of humans chasing anomalies, the AI continuously watches and learns, making it a dynamic and innovative and regulated system. Let's look at the five layer architecture here. Each layer contributes to speed, accuracy, and explainability. Before we dive any deeper, here's a high level of these three, these five layers. The first layer is the data ingestion and pre-processing. In this layer, the workflow cleans and normalizes the fragmented inputs. The second layer is the intelligent semantic under understanding. This is the core layer. Here. The gene AI interprets the data using natural language processing to build accurate claim context. Third layer is the decision engine. Here, the workflow automates the adjudication recommendations and flag any outliers. The fourth layer is the validation and explainability. This layer ensures transparency, auditability, and trust, enabling comprehensive compliance. And the final layer is the integration and analytics. This layer connects the pipeline to enterprise systems and dashboards to deliver real time impact. Let's look at each layer a little more deeply. The biggest barrier we know in any data pipeline is the data fragmentation. Claims come in several formats, like PDFs and hhl, seven messages or even DICOM images. Gene consumes data in all of these formats and converts them into structured data for downstream processing the ingestion pipeline performs data cleaning, deduplication, and enrichment as part of the data, pre-processing steps. Think of it as laying a solid foundation without clean, unified data. No AI model can deliver real reliable outcomes. Once we have clean data, the next challenge is understanding what the data actually means, and that's where the NLP comes into picture. Many of us have seen or heard medical data, and it's not a simple text. It's full of medical codes, aberrations context, and clinical nuances. The second layer leverages natural language processing to extract meaning from these codes and aberrations. JI reads, physician notes, discharge summaries, and claim narratives. Then it identifies entities like diagnosis codes, procedures, and medications, and it does not stop there. It goes on building the contextual understanding. For example, it can distinguish between a follow-up visit for diabetes. Versus a new diagnosis for diabetes. There is a subtle difference that affect the billing accuracy in these two scenarios, and the result is a claim that is enriched with semantic intelligence ready for automated decisioning. Next is the third layer that is validation and enrichment. Here JI validates claims against coding standards like ICD 10 CPT, and Ed ct it crosschecks medical necessity provide credentials and historical patient patterns. The enrichment process then adds auxiliary data, like clinical references, lab results, or previous medical encounters, thus creating a 360 degree view of each and every single claim. The goal here is not to just reject back claims, but to improve the equality before shop submission. And the fourth layer is a decision intelligence. This layer handles what's known as AI driven adjudication. Let's see how this happens. Once the claim is validated, the decision engine determines whether to approve, deny, or flag it for review. It uses reinforcement learning to get better with each iteration, meaning the system improves continuously from expert feedback. We also use precision exception handling, which means identifying those edge cases that cannot be decided or acted upon confidently and routing them to human reviewers. This brings human in the loop along with the AI workflows. The hybrid approach of AI with human in the loop ensures both speed and trust. This brings to the final layer orchestration. In this layer, the entire ecosystem is tied together. Consider this as a control plane that coordinates the various AI models. External data sources and business rules required to automate and expedite the claims adjudication. It manages the APIs, the performance metrics, and integrations with enterprise workflows, whether that's paper payer systems, or EHRs or analytic dashboards. The orchestrator incorporates quality control measures. It can flag claims with high risk factors for human review. Compare AI generated summaries against source documents for accuracy, and even create a feedback loop to continuously improve the model's performance. It provides continuous monitoring, auditing, and traceability, which are the key requirements for regulated environments like healthcare. Now let's talk about the real differentiator, the fraud detection, how fraudulent claims are captured in the traditional systems. The answer to this question depends on the codified static business rules that captures thresholds like flag if the amount is greater than X dollars or duplicate claim within 30 days, et cetera. However, fraud evolves so quickly, often this slightly changing patterns. To bypass those rules, genea brings multimodal analysis. This means it combines a structured data, claim, narratives, and behavioral signals simultaneously in order to detect the suspicious activity proactively. By integrating these different data types, the system gains a more comprehensive understanding than any single source could provide, leading to more accurate and efficient claims adjudication. It can identify sophisticated fraud patterns even before human auditors. Notice them and send the notification to the right person to act on it. For example, the most complex and hard to detect patterns are often worried in unstructured data, like clinical notes, doctors letters, and claim description. NLP could actually reveal a hidden pattern in these unstructured data, like when the description of a patient in the insurance claim uses inconsistent language, conflicting with the factual medical records or hidden linguistic cues such as repeated use of qualifying statements, et cetera. The emerging fraud schemes, frauds and fraudsters have evolved well over the years. In our study, we identified three major classes of these evolving fraud schemes. Number one, the pattern evolution. Fraudsters modify known scams just enough to slip through rules. Example, scammers now use gene AI to create falsified medical records, such as altered MRI scans, x-rays, or clinical nodes to support your fraudulent claims. This makes detecting fraud much more challenging as the supporting documentation appears so legitimate. Using realtime monitoring the JI workflows claims as they're submitted, allowing for immediate intervention before fraudulent payments are dispersed. Next is the collusive networks. This means the providers and patients coordinate claims across institutions. In this case, we use network intelligence where this technology maps relationship between the providers. Patients and clinics to identify collusive fraud rings. It can detect patterns like multiple providers sharing the same address or referring patients in circular patterns. And third one is a behavioral anomalies. Tiny shifts in billing or diagnostic frequency that reveals the abuse of fraud. We use behavioral analytics to capture these types of fraud schemes. Example, a machine learning model. Identify anomalies in billing patterns. By establishing a baseline of normal behavior for each provider, any significant deviation from that baseline such as a certain spike in claims for a specific high cost procedure can be flagged for investigation. JI continuously monitors for these signals using unsupervised learning clustering and pattern evolution analysis. Another innovative capability is synthetic fraud scenario generation. It involves creating a fake identity to submit a claim for reimbursement. The synthetic identity is typically created by combining a stolen social security number with fabricated personal details, making it difficult for the automated systems to even detect the fraud. Using generative modeling, we can simulate fraudulent claims to stress test the detection pipeline to protect against these future threats. This approach helps the system to learn from possible future fraud events, even before attackers, thereby improving resilience, adaptability, and system integrity. Let's look at the challenges in implementing the AI at the enterprise scale. Nowadays, AI has become a part of life for many of us. Building a prototype using AI is quick and easy. However, building a sophisticated AI system to work for us at an enterprise scale doesn't come in a most magical way. Implementing gene AI in enterprise healthcare environments comes with its own hurdles. Number one, the fragmented data silos stakeholders use multiple legacy systems, which has complete interoperability between the systems. Making it hard for the data to interact with each other. That's breaking the data pipeline. We have to overcome these challenges case by cases. For example, building a custom connector to resolve the issue in this case. Number two is a model explainability healthcare decisions, demand transparency, for example. Everyone is interested in knowing why a claim was denied as opposed to if the claim was approved or denied or flagged for review. Number three is a cultural readiness. We have made AI capable enough to build complex workflows, and it's super critical for our human teams to have data and AI literacy to seamlessly navigate the intelligent pipelines built by these AI applications, and also interpret the AI argumented outputs. Overcoming these challenges mainly requires strong data governance, standard interfaces, and human oversight built into these AI enabled workflows. Compliance and privacy. Compliance is not an afterthought in any industry when it comes to healthcare. It's even more essential and critical than it has to be embedded by design. When using gene ai, we need to ensure that the AI outcomes has a following trait. Number one, explainable ai. Where every decision should be traced and explained. And number two is a federated learning. Here models train across institutions without sharing raw data, thereby preserving the privacy. And number three is the bias mitigation, continuous monitoring for demographic fairness, leaving no room even for the unconscious bias. And number four is a regulatory alignment. Here full adherence to the industry regulations like HIPAA for us, GDPR, for Europe, and emerging AI ethics framework. These principles will make the system both responsible and trustworthy, thus still holding the humans accountable. Let's look at the future of healthcare claims. Gen AI is reshaping the healthcare industry at a larger scale in terms of intelligence, efficiency, and performance. Looking ahead, we could leverage AI to have increasingly massive positive impact with the revolutionary processing speed. The end-to-end claim cycles could be reduced from weeks to hours and minutes, and it also supports a substantial cost reduction by automating cuts manual review and re rework there by reducing the cost significantly. And finally, the unraveled fraud detection self-learning models that evolve with threats to handle them effectively and securely. To conclude, we are witnessing how GNI enables s smarter, safer, and more transparent healthcare payment ecosystem. By improving trust between providers, payers, and patients, all alike organization must proactively address the pressing challenges to ensure safe and responsible deployment of gene AI at an enterprise scale. With that gene AI can revolutionize healthcare claims, but combining data intelligence, automation and fraud analytics, all under one explainable and compliant framework, that brings us to our end of our session. Thank you all for your time and interest in listening to today's session. I hope you got a better understanding about the role of Genea and what it holds in the future of claims processing in the healthcare industry. You all have a good day, have a great learning, and cheers.