By Vipin Singh
In recent years, the advancement of AI technology has significantly improved the ability to detect financial fraud in various industries, including the real estate sector. According to a report by Juniper Research, the global AI-based fraud detection market is expected to reach $38 billion by 2026, growing at a compound annual growth rate (CAGR) of 36% from 2020.
In the credit card industry specifically, the use of AI has been instrumental in detecting and preventing fraud. In fact, according to a report by the Nilson Group, AI systems were responsible for detecting over 80% of credit card fraud in 2020. This has resulted in a significant decrease in financial losses due to fraud, with the total amount lost to credit card fraud dropping by 12% in the same year.
These numbers highlight the importance of continued investment in AI technology for fraud detection and prevention. As the threat of financial fraud continues to evolve, it’s crucial to stay ahead of the curve by regularly updating and upgrading AI systems to ensure they are able to accurately detect and prevent fraud in real estate sector, which is a major area of concern.
How AI can be used for fraud detection in a real estate platform:
Transaction monitoring: AI can be used to monitor real estate transactions for suspicious activity. For example, an AI system can analyse data such as the frequency of transactions, the amount of money involved, and the parties involved to identify patterns that may indicate fraud. For instance, if an AI system detects a pattern of large transactions taking place at unusual times or involving unfamiliar parties, it can flag the activity for further investigation.
Identity verification: AI can also be used to verify the identities of parties involved in real estate transactions. By analysing data such as government-issued ID documents, facial recognition, and voice recognition, an AI system can determine whether the parties involved in a transaction are who they claim to be. This is especially useful for detecting identity fraud, which is a common tactic used by fraudsters in the real estate sector.
Credit card fraud: AI can also be used to detect credit card fraud in real estate transactions. For example, an AI system can analyse data such as the amount of money involved in a transaction, the location of the transaction, and the IP address of the device used to make the transaction to identify patterns that may indicate fraud. For instance, if an AI system detects a pattern of transactions taking place in a foreign country using a credit card that is supposed to be used in another country, it can flag the activity for further investigation.
Risk assessment: AI can also be used to assess the risk of fraud in real estate transactions. For example, an AI system can analyse data such as the parties involved in a transaction, their financial history, and the property involved in the transaction to identify patterns that may indicate a high risk of fraud. For instance, if an AI system detects a pattern of transactions involving parties with a history of financial fraud or properties that have been previously involved in fraudulent activity, it can flag the activity for further investigation.
Financial scams use cases on digital platforms:
Identity theft: This happens when a criminal gets hold of personal data—like a credit card number or social security number—and uses it to open accounts or make transactions in another person’s name.
Phishing: This is the practise of using phoney emails or websites to deceive people into supplying personal information or login credentials.
Account takeover: This happens when a fraudster accesses an active account and makes fraudulent purchases using it.
Theft of PII (Personally Identifiable Information): This is when a criminal gets private data, such as a victim’s social security number, credit card number, address, or date of birth.
Ways to address cyber frauds before it happens with the use of AI and ML models:
- Finding trends and irregularities in transaction data that can point to fraud.
- Using multi-factor authentication to lessen the possibility of account hacking.
- Analysing a big quantity of data to quickly identify and stop phishing attempts
- Financial institutions may respond more swiftly and efficiently by automating the process of recognising and flagging suspicious behaviour.
- Phishing assaults may be recognised and avoided using natural language processing (NLP).
However, it’s important to note that AI and ML should not be viewed as a standalone solution for preventing financial fraud in the real estate sector. Instead, they should be used in conjunction with other security measures such as user education and ongoing security audits for a comprehensive and effective approach to fraud detection and prevention.
(The author is Vipin Singh – Head of Technology, Housing.com, PropTiger.com, and Makaan.com, and the views expressed in this article are his own)
