The banking, financial services, and insurance (BFSI) industries are undergoing a significant and rapid change driven by digitalization. This revolution is essential as organizations respond to evolving customer expectations, the need for superior business results, and increasing regulatory requirements. While the banking segment has led in the adoption of digital programs, insurance firms are also ramping up their efforts to catch up.
Mobile and digital initiatives backed by technologies such as artificial intelligence, blockchain, and cloud computing have supercharged two-way communication between BFSI firms and customers. These initiatives allow multichannel access and enable more personalized, insight-driven, and quicker interactions.
However, the increased connectivity has led to an unprecedented level of cyber security risks and threats. This industry bears the highest cost from cybercrime; the nature and volume of transactions make it a natural target. Guarding and defending against these threats have assumed paramount importance.
The BFSI sector in India is evolving at a fast pace, since 2019 there has been a dramatic increase in sophisticated and complex attacks on the BFSI Sector:
Synthetic Identity fraud, called the crime of the new millennium, is one of the fastest-growing problems worldwide. It is a significant example of how fraudsters evolve themselves and focus on weaknesses in online banking security when developing fraud techniques.
In this technique, instead of using a stolen credit card or identity (ID), fraudsters use fabricated, synthetic IDs to draw credit. Indeed, by an estimate, synthetic ID fraud is one of the fastest-growing modern financial crimes worldwide.
Either using a blend of real and fake information or wholly fake personally identifiable information (PII), cyber criminals create a synthetic identity and use this to open an illegitimate bank account. Criminals obtain personal information from customer data breaches and from the Dark Web. Personal information is also gathered through social engineering techniques. This information is then used to open various bank accounts and to get credit cards. The cyber criminals operate these accounts for some time and build up a good credit record of these synthetic IDs.
Detecting synthetic identities has been a major challenge for financial institutions because there is not a single definition or any industry standard to define the legitimacy of an identity. Banks are categorizing the financial losses tied to synthetic identity as “bad debt” therefore it is difficult to consider a fraud timely.
In India, fraudsters create fake Aadhaar cards to create synthetic identities,". In fact, 13.2% of synthetic ID fraud in India comes through Aadhaar cards since the IDs are being used by most institutions as valid identity verification. A Statista report said India has issued 1.3 billion Aadhaar cards to date. The Deloitte Center for Financial Services expects synthetic identity fraud to generate at least US$23 billion in losses by 2030, prompting many banks and fintech’s to develop more advanced biometric security systems to weed out would-be perpetrators.
Reference-https://www.bankinfosecurity.asia/tackling-synthetic-id-fraud-in-era-digital-banking-a-22154
AI Model-The attribute-based threat modeling process involves analyzing domain details, evaluating user profiles and activities, integrating breach history, generating risk scores, and delivering comprehensive reports. This enhances email security by verifying data, cross-referencing breaches, and providing actionable risk analysis.
Our solution takes this approach a step further by verifying comprehensive data, including credentials of lenders, facial recognition, device intelligence, and OCR (optical character recognition). By leveraging real-time assessment and risk-based authentication, our solution enables financial institutions to assign a threat rating to a particular account, allowing them to proactively identify and respond to potential security threats.
In conclusion, the attribute-based threat modeling process, coupled with our comprehensive solution, provides financial institutions with the tools and capabilities needed to enhance email security and mitigate cybersecurity threats effectively. By leveraging advanced technologies and real-time assessment capabilities, organizations can protect sensitive information, safeguard customer data, and maintain the integrity of their systems and infrastructure.
AVendor Due Diligence (VDD) is a financial review of a sales object on behalf of seller which illuminates questions and issues that are relevant to potential buyers of the business.
Third-party vendors have become an essential part of many organizations’ business operations. While working with vendors can help you deliver value to customers, they also pose a significant cyber risk. Vendors tend to have access to critical systems and company data which, if mismanaged, can expose your organization to cyber threats. This is why it is crucial that you conduct vendor cybersecurity due diligence.
Analyses and findings related to financial and tax issues are presented to the seller and are summarized in a report that later is shared with potential buyers. This gives buyers a quick and good understanding of the business’ financial situation and development.
Regulators are now expecting Financial Services Organizations (FSOs) to demonstrate how they take a true risk-based approach (RBA) to Enhanced Due Diligence and Customer Due Diligence to improve its effectiveness in early detection and prevention of financial crime.
As per data maintained by the National Crime Records Bureau, 3,466 and 3,353 cases of online frauds were registered in 2017 and 2018, respectively in India. According to TransUnion CIBIL’s Fraud Trend 2018, there is a significant number of synthetic identity fraud in India also.
Open-Source Intelligence Model Innsight employs Multimodal Analysis, using AI & ML models to categorize content and identify high-risk individuals associated with AML, banking fraud activities flagged across multiple sources.
It navigates the web, indexing data in a Big Data repository, while offering a user-friendly interface for seamless data visualization and streamlined search capabilities. Innsight contextualizing data, adding depth to the analysis of flagged persons. This comprehensive understanding aids in making informed decisions, minimizing risks, and safeguarding BFSI entities' interests.
In conclusion, Innsight represents a paradigm-shifting solution for BFSI entities striving to combat financial crimes effectively. By harnessing Multimodal Analysis and advanced AI/ML models, Innsight empowers organizations to identify and mitigate risks associated with AML and banking fraud. With its robust data analytics capabilities and user-friendly interface, Innsight equips organizations with the necessary tools to safeguard their interests, protect their customers, and uphold the integrity of the financial system.
Money muling, also called mule fraud, is when a person or a business moves or transfers ill-gotten funds for someone else. The owner of the funds may be trying to distance themselves from those assets because they have illegal origins: fraud, or drug or human trafficking. The account used by a money mule is called a mule account.
The ideal money mule is apparently unconnected to the criminal group trying to hide the sources of their money. Relying on a mule creates distance between criminals and their money, making it more difficult for law enforcement to trace money trails.
A mule account or money mule is generally part of the first phase of money laundering, called placement, in which illicit funds are integrated into the financial system. The account may be opened using the mule’s actual personal information or using a synthetic identity.
Once the mule account is opened within a bank or other payment service, money is deposited in that account. The mule then receives instructions on how the funds should be moved and where they should go. Next, the mule transfers the funds to another account. They might also withdraw them as cash to be deposited elsewhere.
In Nov'23 six men, five from Kerala and one from Bengaluru, were arrested on charges of opening mule accounts in different banks and using them for parking money siphoned off during cybercrimes. 126 mule accounts were seized, and these were connected to 75 cases in different parts of the country, including 25 in Karnataka, police said.
Intelligence Fusion Center - Our big data analytics solution provides graph analysis to correlate multiple cues by identifying network of accounts. Our solution offers AI-based behavioral analytics, pattern identification of any transactions, and provides a real-time threat rating to a transaction. This further helps our customers to do risk-based authentication in vendor due diligence.
In essence, our Intelligence Fusion Center offers a comprehensive solution for organizations seeking to enhance their cybersecurity posture and safeguard their operations against financial crimes. Through advanced analytics and real-time threat detection, we empower our customers to stay one step ahead of cybercriminals and protect their interests with confidence.
Authored by-
Shanu Khare
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