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Leveraging AI for Fraud Detection in Fintech: Technical Innovations and Implementation Strategies

The use of AI in banking fraud detection is becoming more and more popular. Indeed, the statistics surrounding online fraud are alarming. Cybercrime imposes a significant cost on the global economy and reaches $600 billion annually, equivalent to 0.8% of the worldwide GDP

Besides, fraud attempts surged by 149% in the first quarter of 2021 compared to the previous year, likely fueled by the rise in online transactions following the COVID-19 pandemic. Consequently, over half of financial institutions have started integrating AI technologies to enhance their capabilities in detecting and preventing fraudulent activities.

Possible Threats

Banks remain prime targets for highly skilled cybercriminals, a trend that has persisted for over a decade. Financial institutions shoulder a significant burden in combating online fraud and theft, investing three times more in cybersecurity than non-financial firms. Bank authorities recognise cybercrime as a "systematic" risk to financial stability.

Nation-states pose the most serious threat due to their access to vast resources, expertise, and limited law enforcement scrutiny. Russia, North Korea, and Iran are identified as highly active in hacking financial institutions, with Chinese espionage remaining prevalent. Iran's recent distributed denial-of-service attack on major US institutions emphasises its intent to exert coercive influence.

The region harbours numerous prolific hackers, potentially aligned with government interests, necessitating adjustments to curb cybercrime's global impact. Failure to address these issues could perpetuate cyber threats worldwide.

There is a threat that cyber attacks can merge organised crime and terrorism into a unified problem. With the proliferation of the Internet of Things, companies are increasingly responsible for safeguarding all their products. Regardless of size, a breach translates to financial losses, compromised data, and damaged trust. Solutions to combat cybercrime include implementing basic security measures like software updates and investing in defensive technologies. 

Collaboration between international law enforcement agencies and the private sector is also important, requiring more resources and improved cooperation, especially in developing countries. Existing procedures like the Mutual Legal Assistance Treaty (MLAT) need enhancement to address modern cyber threats effectively. 

Standardisation and cooperation in cybersecurity standards, particularly in critical sectors like finance, can bolster security. Countries with weak cybercrime legislation contribute to global cyber threats. While conventions like the Budapest Convention have made progress, objections from certain countries highlight the need for new agreements to tackle cybercrime effectively. Additionally, failing governments sheltering cybercriminals should face repercussions, including financial penalties or sanctions, to compel cooperation with law enforcement efforts. Further on we’ll discuss how financial institutions can protect their systems using AI technologies.

AI Technologies as Extra Security Measures

Financial institutions employ a wide array of technologies and practices to provide high levels of protection and at the same time comply with legal requirements. Below I have listed the most common use cases where AI is successfully used:

Document forgery detection

ML algorithms can distinguish between authentic and forged documents. They verify signatures and detect fake identities with high accuracy. Further multi-factor authentication and AI-powered KYC measures can be used to enhance security against forgery.

Credit card theft

By monitoring spending patterns, AI detects unusual transactions in real time and can predict future expenditures and send notifications to users when suspicious behaviour is detected. Thus, they can block their cards and minimise damages.

Phishing attacks

AI algorithms can spot fraudulent emails by analysing subject lines, content, and other details, flagging them as spam to protect users from divulging sensitive financial information.

Identity theft

When cybercriminals hack into accounts and change user credentials, AI can detect unusual activity based on the customer's typical behaviour patterns. It alerts customers and employs multi-factor authentication to prevent identity theft.

In today's rapidly evolving technological landscape, businesses must be agile in embracing digital transformation. Delivering on organisational expectations requires a robust digital mindset fueled by innovation. A suite of services, known as the Live Enterprise, empowers organisations with intuitive decision-making, real-time insights, seamless experiences, and comprehensive data visibility, driving hyper-productivity and collaborative innovation for the future.

Practical Tools for Fraud Detection

Among document verification tools, I can recommend Jumio or Onfido, they easily integrate with banking systems and authenticate identity documents during account opening or loan applications. Signature verification software such as Adobe Sign or DocuSign is also great at helping to authenticate signatures on important documents — loan agreements or account opening forms.

For fraud detection, there are systems specifically tailored for banks, such as FICO Falcon or ACI Worldwide's fraud management solutions; they provide thorough real-time analysis to detect fraudulent activity on credit and debit cards.

One more product worth mentioning is SAS Fraud Detection and Prevention. Established in 1984 as a spinoff from a project at North Carolina State University, SAS is an acronym for Statistical Analysis System. For fraud prevention, SAS focuses on payment fraud and identity fraud, recommending modules like SAS Continuous Monitoring for Procurement Integrity, SAS Detection and Investigation, SAS Identity 360, and SAS Fraud Management. These modules provide centralised monitoring, investigation tools, fraud detection within customer bases, alert and rule management, and leverage various data sources including social media, device fingerprinting, biometrics, and behavioural data. Overall, SAS's solutions capitalise on its extensive data and monitoring capabilities.

Kount —  is another AI-driven fraud prevention platform, designed to detect and prevent fraudulent activities in digital transactions, including e-commerce and online banking. Kount offers a comprehensive strategy for protection against a wide range of threats and it also boasts decades of experience and data collected from various industries worldwide. The biggest advantage Kount provides is robust data to inform its risk assessment processes.

Besides, its users benefit from an intuitive platform equipped with customisable reporting features, allowing them to monitor and analyse data effectively through a user-friendly dashboard. Kant's use of machine learning ensures accurate decisions are made swiftly, thanks to advanced artificial intelligence algorithms.

Moreover, Kount empowers businesses with fully customisable policies tailored to their specific needs, enabling them to make trust and safety decisions aligned with their objectives and risk tolerance levels. Additionally, Kount's automation capabilities reduce manual intervention.

In addition, banks use email security platforms. As an example, I can mention Proofpoint and Barracuda Sentinel. They provide threat protection to banks' email systems by detecting and blocking phishing attempts targeting bank employees or customers. It is advisable to employ 

web filtering apps such as Cisco Umbrella or Zscaler to block access to phishing websites and malicious URLs, thereby protecting customers from falling victim to phishing attacks.

To Wrap Up

On one hand, AI is revolutionising the financial sector, driving efficiency, innovation, and enhanced customer experiences. However, this transformation also introduces new risks and insecurities, as AI-powered technologies become targets for cybercriminals. Fortunately, AI also provides a suite of tools and solutions to minimise these threats and bolster cybersecurity measures. In light of these evolving realities, financial organisations must reassess their policies and procedures to adapt to the changing landscape. This includes implementing new tools and technologies that align with emerging risks and cybersecurity challenges, ensuring the safety and integrity of financial systems and data."




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Adhikar Babu

Adhikar Babu

Product Manager


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26 Mar



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This post is from a series of posts in the group:

Artificial Intelligence and Financial Services

Artificial Intelligence and Financial Services

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