In this interview with Aradhna Sharma, Regional Director at ADVANCE.AI, we investigate how artificial intelligence (AI) can help combat rising online fraud in Singapore.
What are the ways fraud technology can detect fake transactions, phone numbers and/or social media accounts?
Let me give you some simple examples:
- Email address for registration: when was the email created, and is it consistent with registration on other social media platforms?
- Mobile phone status: is it active, is the owner contactable, and is it linked to WhatsApp?
- Social media: are there live, active accounts attached to the email registered?
What is the role of big data and AI within banks, financial institutions and e-commerce, and how can it help manage fraud risk and prevent reputational damage?
At banks and financial services, AI and big data can play a major role across the organisation.
In the front office, use cases include digital identity verification/authentication, e-KYC, and customer/merchant onboarding.
In the middle office, anti-fraud checks and alternative credit scoring are good examples of use cases, while in the back office, they include things like credit risk/underwriting, business intelligence, and customer segmentation.
AI and big data are also relevant to business considerations around reputational damage. For example, if a customer is defrauded or scammed at the end of the day, the question will arise around what preventive/pro-active measures the organisation took to reduce such risks.
Finally, the technologies play an important role in mitigating real risks associated with the financial costs of fraud going undetected for a long time.
What are the other ways AI can help automate digital onboarding/lending, e-KYC authentication and risk management within such sectors?
There are a few other examples worth highlighting:
- Facial verification and liveness detection are applications of AI that can quickly verify and authenticate a person’s identity remotely. It detects high-risk impersonations while reducing the need for physical visits to the bank and facilitates processes like video KYC for bank account openings, loan/deposit applications, virtual payments, and remittances.
- Optical character recognition (OCR) documentation technology is essentially the AI’s ability to quickly verify and authenticate localised documents (e.g. tax and bank statements, identity cards, driving licences, and passports). This reduces the need for manual verification while also providing a superior and faster 24/7 solution with excellent accuracy. Manual resources can then be redeployed to other critical areas of the organisation that require human intervention and decisioning
- A final example to highlight is advanced credit score modelling, which relies on AI to quickly assess and rank creditworthiness and scoring, be it for customers or businesses. This application of AI technology uses a variety of real-time indicators such as device type and usage, telco data, and e-commerce data to help spread financial inclusion, especially in emerging markets where a majority of the population is unbanked or underbanked.
What are the ethics, privacy, and security concerns to govern the use of such technology?
First and foremost, transparency over how data is used and customer consent (for example, through face-to-face consultation that is recorded and documented, or by e-signatures/approvals).
Any data and AI frameworks should be in compliance with existing government laws and regulations that address both data privacy and security.
AI frameworks must be used such that the AI is explainable, transparent, fair, and human-centric to the layman (Singapore is playing a lead role in developing these standards).
Aradhna Sharma has over 12 years of experience in digital transformation, eKYC, compliance, risk and digital identity solutions within the banking, fintech, financial services, insurance and retail sectors.