Insurance companies are now receiving more fraudulent claims than ever before. These fraudulent claims cost insurance companies billions of dollars every year. Fake insurance claims are increasingly becoming commonplace because fraudsters take advantage of manual approaches to claim investigation and resolution.
Deloitte’s Insurance Fraud Survey 2023 identified increased digitization, post-pandemic remote working and weakened controls as key contributing factors behind the uptick in fraud instances.
Insurance companies must reduce their dependence on manual techniques for identifying valid claims. Many insurance companies have already begun leveraging emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to identify fraudulent claims.
Increasing Role of AI in Insurance Sector
CAIF estimates insurance fraud losses of USD 308 billion every year in the USA alone. This obviously compels insurance companies to transmit their losses to American customers in the form of increased premiums.
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AMR reported that AI offerings in global insurance market were valued at USD 2.7 billion in 2021 and are projected to reach USD 45 billion by 2031. This indicates a remarkable CAGR of 33% in the 2021-31 period.
Besides insurance fraud detection and prevention, AI systems and Conversational AI further confer the possibilities of reducing the manual work and improving the experience of genuine customers, whether in terms of better UX or accelerated claim settlement process.
This article will provide a comprehensive guide on how insurance companies leverage conversational AI and how these AI/ ML-based solutions can rely on Graphics Processing Units (GPUs) to undertake insurance claims-related complex data processing tasks.
What is Conversational AI?
Conversational AI is a synthetic program enabling businesses to talk to consumers. These AI-based applications can process, comprehend and respond to human language. Thus, they have widespread applications as virtual agents automating communication through speech (IVR) and text (Chatbot).
These AI systems are developed using Machine Learning (ML) and convoluted Artificial Neural Networks (ANNs) to mimic human-like communication potential. They can efficiently grasp customer intent and decipher customers’ language or context.
The global Conversational AI market was valued at USD 6 billion in 2021 and is projected to post 23% CAGR between 2022 and 2030. Various enterprises, including many in the insurance sector, have begun relying on Conversational AI in chatbots and voice assistants to deliver seamless customer experience and determine customers’ requirements. Within the insurance claim process, these AI can identify fraudulent claims and flag suspicious affirmations. By filtering out the fraudsters, Conversational AI can enhance the claims resolution process for genuine insurance holders.
Insurance Chatbots and their types –
Insurance chatbots are virtual assistants that act as an additional technology-based communication service (available 24×7) in insurance companies. These chatbots now constitute the first line of communication between customers and insurance companies. There are two types of insurance chatbots. This classification depends on how they function.
- Rule-based Insurance Chatbots: These conversational systems can carry out specific discussions with customers, offer support, and process requests based on heuristic constraints. These, thus, help navigate common scenarios and supply pre-loaded standard answers. They cannot deal with convoluted problems like determining insurance fraud and scams.
- AI-Driven Smart Chatbots: These are AI-powered conversation systems trained to carry out human-like conversations. They not only process sequential requests, but also deploy data-driven approaches and analyze communication patterns to flag suspicious responses or fraud attempts. These chatbots need rigorous training through massive datasets of actual and fraud claims. To detect fraudulent claims, these AI systems must learn keywords, phrases, synonyms (Natural Language Processing), acknowledge Frequently Asked Questions (FAQs), and comprehend the customer’s intent. Whereas the initial NLP training process and field deployment for these AI systems can be seamless and quick given the abundance of commercial/opensource training systems, fraud detection and response are an altogether different ballgame and require the Conversational AI to continuously undergo reinforcement learning using real-time chat/ call data. This is the reason that insurance firms prefer GPUs for conversational AI training and modeling.
GPUs & Conversational AI-assisted Fraud Detection
Graphics Processing Units are known to handle large-scale computations by distributing the requisite processing across their multiple cores. These hundreds of parallelly-operating powerful cores are dedicated to accomplishing similar computation tasks simultaneously, thereby manifolds accelerating the AI model training workload.
Detecting insurance frauds, flagging fake claims and identifying scams requires that the underlying Conversational AI systems undergo rigorous training incorporating thousands of case studies, language comprehension and human-independent decision-making. The AI developers must feed these models with various massive datasets, statistics, conversations, situations and simulations to empower them and classify legitimate or fraudulent claims. The initial and ongoing model training requires massive computational power.
GPU systems, whether on-prem or cloud, can deliver the colossal computation resources required for training Conversational AI applications using semi-structured and unstructured data. Most modern GPUs now also come equipped with large on-chip memory, hundreds/ thousands of processing cores, and lightning-fast interconnectivity bandwidth.
How does Conversational AI work (Source: Nvidia)
Benefits of GPU-powered Conversational AI in Insurance Fraud Detection
GPUs have become more commonplace than ever before and almost every industry is leveraging them for their computation and visual depiction needs. Insurance companies are no different. Here are some reasons why insurance companies should invest in GPUs for developing and operating their Conversational AI systems –
(i) Shorter Training Timelines:
Given their multi-core, massively parallel processing architecture, GPUs accelerate AI model development and deployment. Furthermore, the training can be made more exhaustive by gearing the AI model to undergo on-the-job training as well, developing into a more refined version with more experience. The faster the training process, the sooner the system can begin distinguishing new approaches of perpetrating insurance fraud.
(ii) Multi-layered Training Process:
Conversational AI often triggers false positive flags (genuine claim which the AI marked as fraud) that must be vetted and rectified by human insurance agents. Then, based on the insights gleaned, the AI Development team must feed the model new keywords and make changes in the NLP algorithm so that the system can learn from those mistakes, better identify customer intent in the future, and not generate false positive alarms. Such multi-layered training incorporating multiple existing and incoming datasets is simply not feasible without powerful GPU clusters.
(iii) Saves Time:
If you value customers’ time, they will appreciate your business. Conversational AIs are intelligent chatbots that can understand human intentions and motives. But behind the scenes run several Artificial Neural Network algorithms. If the conversational AI responds slowly, it will degrade the customer experience. GPUs can expedite the response time of the Conversational AI and allow it to answer standard queries in almost-human manner.
(iv) Highly Scalable:
Cloud GPU servers are optimized for diverse workloads and enterprise-grade data processing. These are dynamically scalable depending on the requirement of insurance companies, for instance following disasters/ pandemics when more people are filing claims.
(v) 24×7 Availability:
GPU-powered Conversational AI offers 24×7 support so that your customers can reach you as per their convenience. It reduces wait time and missed conversations. To your customers, your business is always online, even when your employees are off duty.
Better customer experience => better goodwill => more business.
Best GPUs for Conversational AI?
It is imperative for software developers to select optimum GPU resources for their Conversational AI/ Chatbot projects, especially when catering to incredibly price-competitive businesses which are also highly sensitive towards customer privacy concerns (such as insurance services).
Most analysts believe that leading GPU manufacturer Nvidia is best placed to capitalize on the emerging conversational AI and chatbot ecosystem as it dominates roughly 80% of the GPU market. This is reflected in Nvidia’s sales indices across geographies.
No wonder that ChatGPT’s meteoric rise is fueled by Nvidia’s top-of-the-line A100 GPUs. Each A100 accelerator (priced at a whooping 10,000 USD!) comes equipped with cutting-edge Tensor cores, Ray Tracing capabilities, and 80 GB on-chip HBM2 memory capable of delivering up to 2TBps memory bandwidth.
Nvidia’s DGX-A100 servers which come equipped with 8 A100-80GB GPUs can effortlessly handle language models with up to 20 billion parameters. On the other hand, GPT-3 has 175 billion parameters! Training GPT-3 on a single V100 GPU, the previous generation of Nvidia machines would’ve required 288 years!
Nvidia’s A30 and A2 GPUs are among other substantial machines that insurance firms can opt for conversational AI and intelligent chatbots. These GPUs are especially well-suited for AI/ ML development and can facilitate High-Performance Computing to provide a smooth, lag-free workflow for colossal workloads.
Conclusion
Leveraging AI/ ML, NLP, HPC and Big Data analytics has become an imperative in extremely cut-throat industries like financial services and insurance that are also simultaneously vulnerable to frauds and scams of all kinds. Developing and deploying a top-class conversational AI service to identify fraudulent/ suspicious claims in real-time can remediate many of the challenges faced by insurance-sector organizations.
We’ve discussed how GPUs are instrumental, or even indispensable, for developing a properly-functioning conversational AI model. But before taking a plunge and investing in on-premises GPU systems, it can be a good idea to avail Cloud GPU resources available on a pay-as-you-go basis. Still on the wall about how to proceed? Book a call with our consultant at +91-789-789-0752and see how we can make your job easier.
