Artificial Intelligence in the Face of Financial Fraud

Press Release
GDS Modellica
The emergence of AI has also given rise to more complex and sophisticated financial crimes that require the application of new tools in fraud prevention.
Artificial Intelligence will be a key tool in helping detect fraudulent activities among millions of daily financial transactions.
The financial sector has always been a target for fraud due to the high value and importance of financial transactions. In this new era, institutions have implemented new tools, solutions, and measures to prevent and combat fraud. Fraudulent activity is becoming increasingly complex and sophisticated, as scammers sharpen their wit and strategies to bypass systems and avoid detection. Technological advancements allow for the rapid and efficient processing, storage, and access to vast amounts of data, thanks to increased usage and precise and efficient functionalities.

According to the True Cost of Financial Crime Compliance Study by LexisNexis, in 2020, these entities spent over 214 billion euros on financial crime prevention and addressing complexity of threats. Financial crimes are continuous and persistent, and the consequences are real and tangible. Hence, the need to design and incorporate technological tools capable of combating financial crime. Artificial Intelligence (AI) and machine learning algorithms facilitate this task by accurately analyzing the enormous amount of data generated in any financial action.

In this sense, technology is an essential element for financial firms to conduct their activities reliably, securely, and in line with their business model. AI proves to be a key ally for the different entities within the financial ecosystem. Its potential and application are unquestionable in the development and execution of financial services, especially in areas related to credit risk and loan decisions, fraud prevention and anomaly detection, portfolio management for cross-selling or upselling, among others. In response to the challenges presented, GDS MODELLICA has developed Modellica Fraud Engine, a technological solution for fraud detection. MFE provides a unique view of identity for customer acquisition, detects fraud, and enables organizations to build trust and security on the path to digital transformation.
Despite the numerous existing safeguards, banks are more exposed to fraud than ever, partly due to the growing number of channels customers use to access services, such as online banking and mobile applications. According to Antonio García Rouco, CEO of GDS Modellica, “regulation, compliance, and overall risk management pose a significant operational burden for financial services. Fraud in the onboarding process, such as opening a credit or bank account with fraudulent or forged information, is an increasingly significant problem. Banks lose billions each year due to fraudsters using stolen personal information to create new accounts and commit fraudulent acts.”

For years, GDS MODELLICA has developed technologies and solutions that help financial institutions, Fintech companies, insurers, or utility service providers like electricity or water companies, prevent and combat fraud with a comprehensive approach. It provides security that these institutions extend to their customers, who are often the ones exposed to these frauds. Regulatory bodies and companies must ensure the proper functioning of payment systems.

The implementation of new tools and technology, as García Rouco states, “has made it possible to face one of the most significant challenges: detecting fraud and identifying illicit activity among millions of daily transactions.” Preventing and mitigating the effects of financial crimes and scams is crucial not only for financial institutions to continue their operations as usual, avoid fines, and maintain a good reputation among their customers. Artificial Intelligence is revolutionizing and transforming all economic sectors, including finance and fraud prevention. In addition to prevention, AI streamlines overall financial operational processes by expediting decision-making and accurately prioritizing and investigating each transaction. Its incorporation has positive effects on financial institutions by improving the customer experience and reinforcing relationships with personalized actions.
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