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Global insurer BNP Paribas Cardif cuts claims processing with AI

Documents bedevil the lives of customers and the organizations that rely on them. However, in some vertical markets, the document is a necessary evil if accurate and compliant services are to be delivered. Insurance is just one example; the sector is document-rich or document-burdened, depending on your point of view. The complexity of the sector and the individual needs of customers make documenting fundamental. Surely, artificial intelligence (AI) is ripe for managing documents for the benefit of the insurance provider and the customer. The short answer is yes, as data leader Sébastien Conort, the chief data scientist of BNP Paribas Cardif, revealed. With over 80 million customers in 30 nations and more than 500 distribution partners it is easy to see how BNP Paribas Cardif has a forest of documents to manage. The Paris headquartered business sells insurance as a white-label product through the automotive industry, banks, retailers, telcos and insurance brokers in Asia, Europe and Latin America. In 2023, BNP Paribas Cardif had written premiums to a value of €30.3 billion and has been actively growing as a business, acquiring 51% of Italian insurance firm BCC Vita last November and began talks to acquire Neuflize OBC from ABN Amro in May. For financial services companies, documents form the core of their relationship with the customer, especially in insurance, as Conort explains: In the insurance business, we have two important customer touchpoints: the sale and when they have to open a claim. These are infrequent. Yet, across a business of the scale of BNP Paribas Cardif, those infrequent interactions mount up, and the chief data scientist says the business processes 100 million documents a year. Conort adds: Being excellent at document processing is a differentiator; it impacts the customer and lowers the operational cost of the business. Coping with high demand and seeking to improve customer services led BNP Paribas Cardif to realize the documents and the processes around those documents were a core business value. Automation would enable the business to derive increased value, such as data, from its document processing, but that automation had to be cost-effective. Since 2021, BNP Paribas Cardif has operated Intelligent Document Processing (IDP), an automated document processing platform that uses a raft of technologies, including AI. Conort says of the platform: IDP augments our collaboration with operational departments, and it relieves them from tasks that are not added value, so they can concentrate on having more interactions with customers. Using optical recognition, natural language processing, information extraction, data matching and a data quality assessment set of technologies, IDP reduces manual tasks for teams that are either dealing with a claim, managing relations with third-party retailers or selling insurance to a customer. Conort says: IDP means we have faster processing for our customers, as we are using AI to make faster decisions. The addition of AI modelling has enabled BNP Paribas Cardif to model its claims and discover which types of claims require less documentation, making the claim process less stressful for the customer and faster for the business. AI is not the only way the insurer has reduced its reliance on documentation, it has assessed when its administration partners offer documents via application programming interface (API) systems, this again reduces the need to contact the customer and ask for further documentation. However, he adds: We never refuse a claim based on AI. Claims that are not automatically accepted by AI go through usual human review. Embedding AI into IDP has given the business confidence to increase its experimentation with AI, Conort says: This is one of the first use cases of AI for us, and it proves that it brings value, and we will try to think of areas that AI can bring more value and improve services. BNP Paribas Cardif has publicly stated its AI strategy is to turn customer data into value for the customer through three pillars: data quality, data protection and data exploitation, and that the Corporate Analytics team that Conort is part of is the center of building, operating and reusing algorithms. Corporate Analytics is involved in the Scikit-Learn open source skills community, and works closely with supplier Domino as the international platform for what the business calls industrial data science management. These are central to that AI strategy. As machine learning engineering director and chief data scientist, Conort worked closely with IT to develop IDP, with the development being run as a joint venture between his analytics division and IT. A key technology partner has been machine learning operations provider Domino. In the BNP Paribas Cardif data science department 100 team members use the Domino Enterprise AI Platform to share their work, which reduces duplication and creates a better community within data science. Two years in, and IDP is delivering business benefits, Conort says: In Spain, we have managed to reduce the claims process from seven days to just one, and in Chile, we have reduced the number of claims documents from six to three. In all the AI hype, use cases are going to become increasingly important to digital leaders. Incorporating AI into a wider automation programme, with clear benefits to the customer and, therefore, the business, is exactly the type of step change that AI can and will deliver to organisations. BNP Paribas Cardif has clearly understood customer and business challenges, as well as concerns about AI and deployed AI in a way that cuts manual processes. What will be interesting to see over the longer term is how this usage of AI permeates across the business, as well as how the technology impacts the energy usage of a major international financial services firm.

diginomica

Tue, 13 Aug, 2:05 PM UTC

Takeover Terminated: Preventing Life Insurance Account Takeover with Data and AI

This article originally appeared in the Anti-Fraud Alliance email newsletter and is re-published here with permission. Life insurance fraud is the second most expensive type of insurance fraud in the United States, with losses totaling an estimated $74.4 billion per year. Anecdotally, many carriers note that, while insurance fraud detection rates are falling, they fear that it may be because flocks of fraudsters equipped with AI are flying under the radar. Certainly, life insurers make for appealing fraud targets. Life insurance policies, annuities and retirement account values tend to be higher than the checking or savings accounts held at traditional banks. Adding to the allure, insurers of all kinds have historically invested far less in robust fraud defenses than their banking counterparts. Many carriers, even the well-established players, still rely on antiquated fraud controls like knowledge-based authentication questions to reroute emerging fraud threats. One particular attack vector troubling insurers in the wake of increased digitalization is life insurance account takeover (ATO). The Life Insurance Marketing and Research Association's (LIMRA) 2023 FraudShare annual report found that life insurance ATO incidents grew by 13% in 2023, with more than nine ATO incidents detected each month on average. It's little wonder; committing life insurance fraud is easier than ever. With the popularization of readily accessible generative AI tools, everything that makes artificial intelligence an effective tool to combat fraud is being weaponized by bad actors at every turn. Even "citizen fraudsters" can wield sophisticated deepfake technology, which requires little to no AI skills, to successfully defraud life insurers. Two virtual checkpoints in the life insurance ecosystem are particularly vulnerable to exploitation: As ATO attacks climb, securing these facets of the policyholder journey has become an imperative. Trends in fraud-fighting technology A recent cross-industry anti-fraud technology study by SAS and the Association of Certified Fraud Examiners (ACFE) revealed that automated business rules and anomaly detection/exception reporting are the data analysis techniques most prevalently used by insurers to combat fraud, cited by 81% and 62% of insurance respondents respectively. Unfortunately, neither of these techniques is altogether effective at detecting AI-powered ATO attacks, because such attacks are designed to mimic a legitimate customer or policyholder. Unraveling supercharged ATO schemes requires higher caliber anti-fraud technology. So, where do insurers sit on the advanced analytics maturity curve? The ACFE and SAS' joint research found that: Most carriers aren't using AI- and ML-fueled countermeasures to combat ATO and other emerging fraud threats - or at least not to the same degree that fraudsters are applying them to criminal ends. Turning the tables requires fighting AI...with AI. Seizing the machine learning advantage Machine learning (ML) is a form of AI with enormous potential in unraveling advanced fraud modalities. ML algorithms learn from data, identify patterns and make decisions with minimal human intervention, making them adaptable in thwarting increasingly sophisticated and novel attack vectors. With ML, a life insurer can authenticate the policyholder behind every device, while enhancing its ability to move good customers through their digital journey faster and deliver a "wow" experience. As a further benefit, the reduction in false positives can help bolster the insurer's brand reputation and target state actors and citizen fraudsters alike with higher rates of success. ML is especially suited for this task, because it employs champion and challenger models and A/B testing of data and service providers. Insurers can compare approaches to a comprehensive and impactful identity strategy and deploy what works best in seconds. With ML, insurers can take advantage of a reliable and scalable identity-proofing strategy. The trouble with data Insurers often face considerable challenges in implementing an AI-powered plan of attack. Among them, the SAS-ACFE study revealed that eight in 10 insurance respondents named poor data quality or integration a moderate (48%) to major (32%) challenge - second only to budgetary restrictions, cited by 85% of respondents. More broadly than data quality and integration, data orchestration cannot to be overlooked. Multiple disparate data sources must be harnessed to develop optimally robust capabilities to effectively address ATO fraud at vulnerable points of access. These data sources could include device biometrics, public records, account details and more. Consortium data can be another impactful tool. Of course, it's not enough to simply have tons of good data. All this information needs to be coordinated in real-time during a login, application or other engagement. By choosing the right platform, insurers can readily establish a durable data ecosystem and deploy sufficiently advanced AI/ML capabilities to address not only fraud risks but other business needs across risk management and customer engagement. Conclusion Protecting customer assets is a foremost priority for life insurers. To terminate account takeover and prevent and detect other forms of life insurance fraud, carriers and antifraud practitioners must prepare to meet in the virtual field of combat with all the technological ingenuity fraudsters possess -- and all the integrity they lack.

Finextra Research

Fri, 30 Aug, 2:03 PM UTC

Use Case of Claims Management System in Insurance Business?

Claims management software is revolutionizing the insurance industry. These advanced digital tools are streamlining operations, enhancing accuracy, and dramatically improving customer experiences. By automating complex processes and leveraging data analytics, claims management software is enabling insurers to process claims faster and more efficiently than ever before. The impact of this technology extends far beyond speed and efficiency. Claims management software is transforming how insurers interact with their customers during what is often a stressful time. It provides real-time updates, simplifies document submission, and allows for more personalized service. This shift towards a more customer-centric approach is helping insurers build trust and loyalty in an increasingly competitive market. As artificial intelligence and machine learning capabilities continue to evolve, the potential for claims adjuster software to further transform the industry is immense. From fraud detection to predicting claim outcomes, these tools are equipping insurers with powerful insights that can drive strategic decision-making and improve overall business performance. Claims management software is revolutionizing insurance industry operations. It enhances efficiency, streamlines processes, improves fraud detection, and reduces costs through automation. Claims management systems boost operational efficiency and productivity. These platforms centralize data, automate routine tasks, and provide real-time information access. Insurers can process claims faster, reducing backlogs and improving customer satisfaction. Workflow automation tools route claims to appropriate handlers, eliminating manual assignment delays. Document management features organize and digitize paperwork, making retrieval quick and easy. Machine learning algorithms analyze historical data to identify patterns and suggest optimal claim handling strategies. This aids in faster, more accurate decision-making by claims professionals. Claims automation software significantly reduces processing times. It captures and validates claim data electronically, minimizing manual data entry errors and speeding up initial assessments. Structured data collection allows for quick categorization and prioritization of claims. Automated rules engines apply policy terms and conditions consistently, reducing the need for manual review of straightforward cases. IoT devices and mobile apps enable policyholders to submit claims instantly with photos and videos, accelerating the process from the start. Real-time information sharing between all parties involved in a claim facilitates faster resolution. Advanced analytics and machine learning algorithms in claims management systems enhance fraud detection capabilities. These tools analyze vast amounts of data to identify suspicious patterns and anomalies that may indicate fraudulent activity. Risk scoring models assess claims in real-time, flagging high-risk cases for further investigation. This allows insurers to focus resources on potentially problematic claims while fast-tracking genuine ones. Network analysis techniques help uncover hidden connections between seemingly unrelated claims, exposing organized fraud rings. Predictive modeling assists in identifying emerging fraud trends, enabling proactive countermeasures. Automation in claims management leads to significant cost savings. By reducing manual labor and paperwork, insurers can process more claims with fewer staff, lowering operational expenses. Self-service portals allow policyholders to file and track claims without direct interaction with claims handlers, reducing call center volumes. Automated payment systems speed up settlements and cut administrative costs. AI-powered chatbots handle routine inquiries, freeing up human agents for complex cases. This improves resource allocation and reduces overall claim handling expenses. Data analytics tools provide insights into claim trends, helping insurers optimize their processes and identify areas for further cost reduction. Claims management software is transforming how insurers engage with policyholders during the claims process. These innovative systems leverage AI, omnichannel support, and user-friendly interfaces to enhance communication, build trust, and deliver seamless digital experiences. AI-powered chatbots now handle routine customer queries, providing instant responses 24/7. This frees up human agents to focus on complex cases, improving overall efficiency. Omnichannel support allows customers to seamlessly switch between communication methods like phone, email, and messaging apps. Insurance companies use AI tools to analyze customer data and personalize interactions. This leads to more relevant and timely communications throughout the claims journey. Case management systems integrated with claims software enable agents to access all relevant information in one place. This results in faster, more accurate responses to customer inquiries. Modern claims management platforms offer policyholders real-time visibility into their claim status. Customers can track progress, view required documents, and receive automated updates through secure online portals. These systems replace opaque manual processes with clear, step-by-step guided workflows. This transparency helps build trust between insurers and claimants. Digital tools allow for easy document submission and electronic signatures, eliminating paperwork delays. Secure file-sharing features protect sensitive information while facilitating smoother collaboration. Insurance companies now offer mobile apps that allow customers to file claims, upload photos, and communicate with adjusters directly from their smartphones. This convenience significantly improves the claims experience. Intuitive interfaces guide users through each stage of the claims process. Clear instructions and helpful tooltips reduce confusion and minimize errors in claim submissions. AI-powered virtual assistants can answer common questions and provide guidance, enhancing the self-service experience. These tools help customers navigate the claims journey with minimal frustration. By prioritizing user-friendly design, insurers create a competitive edge in customer satisfaction and retention.

Finextra Research

Sun, 8 Sept, 6:00 PM UTC

From Smart Automation to Strategic Insights - Start-ups Using AI to Redefine Fintech Solutions

The fintech industry is undergoing a revolutionary transformation, driven by the power of artificial intelligence, hence AI. As we venture deeper into the digital age, innovative startups are leveraging AI to redefine financial services, from automating complex processes to providing personalized insights. These advancements are not just enriching existing systems, they're completely reshaping how we interact with money and financial institutions. At the forefront of financial automation, Panax is making waves with its AI-powered ERP tagging system. By automating data classification and extraction, Panax is streamlining financial operations for businesses of all sizes. Panax's system goes beyond simple automation, utilizing machine learning algorithms to continuously improve its tagging accuracy. This innovative approach not only saves time but also significantly reduces errors, allowing companies to make more informed financial decisions based on accurately categorized data. Codat is tackling one of the biggest challenges in fintech: seamless data sharing. By integrating AI into its platform, Codat is simplifying the complex process of sharing accounting and financial data between fintech companies and small to medium-sized enterprises (SMEs). The platform's AI-driven approach provides real-time updates, ensuring that financial information from various sources and formats is standardized and goes beyond mere data transfer, offering intelligent data normalization and reconciliation. Imagine a banking assistant that understands context, learns from interactions, and provides personalized advice. That's exactly what Kasisto offers with its cutting-edge conversational AI platform. Kasisto empowers banks to elevate their customer service to unprecedented levels with its AI-driven solution that doesn't just answer questions, it adapts to individual preferences, making each conversation more relevant and insightful. As a result, banks can foster stronger, more meaningful relationships with their clients, driving customer loyalty, satisfaction, and trust. Tink's AI-powered platform shakes up the financial landscape by transforming open banking into a personalized experience. The platform aggregates data from various financial sources, giving its users actionable insights, enabling them to make informed decisions about their money. It not only enhances financial literacy but also drives innovation in the banking industry. Financial institutions can leverage Tink's technology to offer tailored products and services, fostering stronger customer relationships and driving revenue growth. Tink is paving the way for a more transparent, efficient, and personalized financial future. Credit decisions have traditionally been based on limited data points, often excluding deserving borrowers due to outdated or narrow evaluation methods. Zest AI is converting this narrative by operating advanced machine learning algorithms to analyze a much broader and more diverse set of data. Its results in more accurate, fair, and inclusive lending decisions that consider factors beyond traditional credit scores. Zest AI's approach not only opens the door for more consumers to access credit, but also helps financial institutions mitigate risk, make smarter decisions, and expand their customer base. In the ongoing battle against money laundering, it stands out as a game-changer with its AI-powered anti-money laundering (AML) solutions. Ayasdi's leveraging technology with advanced machine learning algorithms and sophisticated data analysis can identify suspicious activities with a level of precision far beyond traditional methods. This technology enables financial institutions to detect complex patterns of illicit behavior that might otherwise go unnoticed, ensuring compliance with stringent regulations. Ayasdi's AI-driven approach not only enhances the accuracy of AML efforts but also reduces false positives, saving time and resources. In turn, financial institutions can protect their assets more effectively, minimize risk, and bolster their defenses against ever-evolving financial crimes, safeguarding both their reputation and the global financial system. Although not solely a fintech startup, OpenAI Codex is making a substantial impact on the industry. By offering AI-powered coding assistance, Codex is revolutionizing software development processes, allowing fintech companies to accelerate their innovation cycles. This speedup enables the rapid creation and deployment of new financial technologies, driving efficiency and enhancing the user experience. Codex's contributions are demonstrating the transformative potential of AI in fintech, paving the way for a future of more advanced and accessible financial solutions. Tractable's AI-powered solution is revolutionizing the insurance industry by automating real-time damage assessments from images. This innovative technology is streamlining the claims process, making it significantly faster and more accurate. For consumers, this translates to quicker claim resolutions and a more seamless experience. Insurers, on the other hand, can benefit from reduced fraud, operational costs, and improved customer satisfaction. Tractable's technology is demonstrating the transformative potential of AI in the insurance sector, paving the way for a more efficient and equitable industry. Anodot's AI-powered solution is revolutionizing fraud prevention and risk management by providing real-time anomaly detection in payment processing. With advanced machine learning algorithms, Anodot can identify and address potential threats before they can cause harm, safeguarding both consumers and businesses. This proactive approach is essential for maintaining trust in digital financial systems and securing a reliable payment environment. Anodot's technology is a testament to the transformative power of AI in enhancing cybersecurity and protecting the integrity of financial transactions. Nomi Health is altering dramatically the healthcare industry using the power of AI to streamline and optimize billing and payment processes. Nomi Health is bringing much-needed transparency and efficiency to healthcare financial operations. They not only benefit healthcare providers by reducing administrative burdens and improving earnings cycles but also enhance the patient experience by simplifying the often complex and confusing world of medical billing. Nomi Health's clever solution is paving the way for a more efficient, transparent, and patient-centric healthcare system. As these startups demonstrate, AI is not just improving existing financial services, it's completely redefining them. From smarter automation to more strategic insights, AI is helping fintech companies to offer more personalized, efficient, and secure services than ever before. The future of fintech is undoubtedly intertwined with AI. As these technologies continue to evolve, we can expect even more innovative solutions that will further transform how we interact with financial services. Whether it's through more accurate risk assessments, enhanced fraud detection, or personalized financial advice, AI-powered fintech is set to make financial services more accessible, efficient, and tailored to individual needs.

Finextra Research

Tue, 24 Sept, 2:00 PM UTC

Cyber insurance: Aflac fights threats with AI - SiliconANGLE

Cyber insurance and AI collide: Aflac leading the charge against evolving threats As cyber threats become more sophisticated, insurance companies are evolving to safeguard customers' data and financial well-being. American Family Life Assurance Company of Columbus is leading this transformation by integrating cybersecurity into its core services, offering enhanced cyber insurance solutions. Through advanced fraud detection technologies, Aflac is addressing the growing risk of phishing attacks and identity theft, positioning itself as a proactive defender in an increasingly vulnerable digital world. This approach showcases how cyber insurance is becoming a crucial tool for modern insurers in combating emerging online threats. "We have to make sure that adversaries aren't trying to use that brand to promote and further their attacks," said DJ Goldsworthy (pictured), vice president and global practice lead of security operations and threat management at Aflac. "Things like phishing schemes and other schemes and employment fraud schemes, things like that. Because it's a household brand, it puts us into the scope of some of those types of adversarial activities." Goldsworthy spoke with theCUBE Research's Dave Vellante and Rebecca Knight at Fal.Con, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed Aflac's approach that involves staying proactive and strategic partnerships to combine cybersecurity and insurance. (* Disclosure below.) Generative AI is transforming the cybersecurity landscape for attackers and defenders. It enhances the sophistication and speed of cyberattacks, enabling adversaries to gather vast amounts of information and exploit vulnerabilities faster than ever before. This makes it imperative for Aflac to leverage AI in its own defenses, creating intelligent systems capable of detecting and responding to threats at the same speed as attackers, according to Goldsworthy. "Speed is always a challenge in cyber because the faster that adversaries can exploit vulnerabilities, the faster you have to be at identifying them and fixing them," he said. "They're also going to become more sophisticated because they're going to be able to gather large amounts of information on our identities, social media and information about the company." By consolidating vendors, Aflac has streamlined its security processes, reduced overhead and enhanced interoperability between tools. A more consolidated platform enables seamless communication between tools and ensures more effective detection and disruption of attacks, according to Goldsworthy. "You're doing all this integration work to make tool A work with tool B, which then works with tool C," he said. "Are they sharing the right information and do those integrations work? All the maintenance around that. Most teams struggle to get that right. That's why I think we see these multi-million dollar investments in cybersecurity and breaches are still happening." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of Fal.Con:

SiliconANGLE

Fri, 4 Oct, 10:14 PM UTC

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