Artificial intelligence (AI) is transforming the insurance sector, becoming a key tool to detect and combat fraud. Its ability to analyze large volumes of data and recognize suspicious patterns allows companies to reduce millionaire losses and expedite accident management. According to sector studies, the application of AI in car insurance and property can increase fraud detection and improve recovery rates, reinforcing the solvency and efficiency of insurers.
Spanish insurers are adopting technologies such as ‘Machine Learning’, neuronal networks and natural language processing to reinforce fraud detection, explains Raúl Santiago, CDO of Capgemini Financial Services in Spain. These tools allow analyzing large volumes of real -time data and integrating information from social networks and external bases, he adds. Many companies have already implemented these solutions and have incorporated specialized platforms to improve their effectiveness, he says.
«With the emergence of generative artificial intelligence in recent times, insurers are evaluating their potential to improve the detection of fraud patterns and documentary analysis. Facing 2025, they will also explore the use of generative AI agents to develop more autonomous and autonomous fraud detection flows, ”says Santiago.
Beyond the identification of fraudulent patterns, AI is allowing insurers to deal with increasingly sophisticated threats, says Alex Borrell, head of Insurance in Accenture in Spain and Portugal. The use of false identities generated by AI, the manipulation of images and improper access to systems are some of the new risks facing the sector, he says.
To combat them, companies have begun to apply in the detection of ‘Deepfakes’ in the’ onboarding of customers, the analysis of suspicious behaviors and the identification of alterations in images and videos. This technology is also optimizing the liquidation of claims and reducing operational costs, adds Borrell.
It is better to prevent
Insurers are not only using generative to detect fraud in real time, but also to anticipate them before they occur, says Cristina Álvarez, director of generative generative in Insurance of NTT Data. Techniques such as predictive analysis allow identifying suspicious patterns and proactivelyhe states. The generation of synthetic data facilitates the training of advanced models without compromising privacy, while algorithms continually evolve to adapt to new fraudulent strategies. These solutions have allowed us to reduce fraud by up to 40%, optimizing resources and reinforcing the financial security of the sector.
In parallel, the generative AI is promoting the development of Smart agents They automate complex tasks in the detection of fraud, explains Álvarez. These systems analyze real -time claims, generate detailed reports and allow human researchers to focus on strategic cases. They also improve forensic document analysis, detecting inconsistencies in images or texts and verifying data with external sources. In addition, advanced fraud simulations perfect detection models and reinforce the ability of insurers to identify new fraudulent tactics.
Beyond technological innovations, some insurers have developed their own strategies to reinforce the fight against fraud. Santalucía has implemented in the last five years a comprehensive system of detection and management of fraud based on AI, advanced analytics and control technology, with the objective of Reduce fraudulent claims and optimize the operation, says Israel García, manager of the Central Fraud Unit.
The insurer uses models that identify suspicious patterns and alert possible risks, combining automated analysis with the supervision of a specialized team. This strategy has allowed minimizing false positives, reducing response times and improving fraud prevention from the hiring of policies, reinforcing the efficiency and security of the process, explains Garcia.
On the other hand, Mapfre has reinforced its global strategy in the detection of fraud through artificial intelligence in markets such as the United States, Spain and Brazil, as explained in statements sent to ABC. In the US, their ‘Advanced Analytics’ and ‘Technical Claims’ teams have developed a system based on ‘Machine Learning’ and graph analysis to identify suspicious patterns in accidents, first in cars and then in home.
Optimization
The insurer emphasizes that its wide volume of historical data, together with a responsible governance model, has allowed improving detection, minimizing false positives and expediting claims management. With the advance of generative and multimodal AI, its models have increased its effectiveness, optimizing processes without affecting the customer experience, they say.
Managing these challenges requires an effective strategy of data administration, a key aspect for companies such as Caser. For years, the insurer has worked on structuring traditionally disorderly information, such as calls or peritations, to improve the use of artificial intelligence in their processes, explains Francisco Picón, director of general insurance benefits.
Thanks to this preparation, it has developed advanced tools that combine automated analysis with the experience of the processors, which allows to evaluate with greater precision The probability of fraud. However, fraud constantly evolves, which forces predictive systems to update and train the equipment to improve automatic detection against new threats, says Picón.
Raúl Santiago, from Capgemini, warns that the implementation of AI in fraud detection continues to face key obstacles. Insurers must deal with little flexible inherited systems, high modernization costs and incomplete databases that limit the precision of predictive models. In addition, the difficulties in accessing external sources and the shortage of specialized talent, together with regulatory restrictions, continue to slow down their adoption. To overcome these challenges, the sector must bet on more agile strategies and a modernization that guarantees the full integration of AI in the fight against fraud, he says.
New era
In this transformation process, IA is also redefining operational efficiency and relationship with customers, they stand out from Mapfre. Beyond fraud, the insurer bets on this technology to optimize customer service and expedite processes, combining Automation and human supervision. Tools such as conversational assistants and advanced analysis improve precision in accident management and risk assessment. However, its implementation follows a responsible approach, ensuring that decision making maintains a balance between technology and human criteria, they explain.
As IA is consolidated in the insurance sector, other emerging technologies are gaining prominence in the fight against fraud, says Alex Borrell, from Accenture. Blockchain improves identity verification and reduces intermediaries, quantum computing reinforces encryption and optimizes predictive models, real -time geolocation allows validation of accidents and social engineering helps detect stolen credentials and synthetic identities. The combination of these tools with Ia promises to strengthen anti -fraud systems and raise the safety of the sector, explains.
«A key element in this fight against fraud is the collaboration in the insurance sector, backed by organizations such as Unespa and Icea. In recent years, this cooperation has resulted in the creation of a sector anti -fraud that covers household insurance, communities and shops. This file has proven effective in preventing fraud in duplicate sinister among several companies, a common practice among criminal organizations dedicated to this type of scam, ”he concludes.
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