The LendTech Collective
Insights to stay ahead!
Bimonthly Newsletter | July 2022 | Issue 119
Featuring
- Intelligent Security – taking digital identity verification to the next level with AI
- How is AI transforming fraud detection in the financial services industry?
- In the News
- Major Events
Focus On
Intelligent Security – taking digital identity verification to the next level with AI
Loan application fraud is a lender’s nightmare. It’s a real issue faced by many financial firms. It is estimated that financial institutions lose billions of dollars yearly to this type of scheme, with synthetic identity fraud alone being responsible for over six billion dollars of credit losses. Without face-to-face interactions, fraudsters and thieves attempt to use stolen identities and fictional financial data to commit online financial crimes — believing it to be an easier or more successful prospect.
Application and identity fraud prevention
When fraud happens, it comes with a cost to lenders. In the US alone, close to 300,000 people fall victim to credit fraud every year. In this situation, fraud mitigation must be an integral part of any lending risk management plan.
To achieve a successful fraud prevention strategy, FIs must conduct a balancing act between security and customer experience. They need to put in place an account opening process that includes real-time risk assessment and identity verification while delivering a digitally seamless customer experience. The importance of improving the customer experience cannot be understated. It is best for businesses to guarantee strong identity verification and fraud prevention, as every little mistake in this process can put the customer at risk and damage the reputation of the business. Ensuring a secure account sign-in process builds trust with users and attracts and retains more customers.
FIs can battle fraud on loan applications in a wide variety of ways:
- In-depth monitoring of new account application data
- Monitoring of existing accounts for suspicious activity patterns
- Identity verification to prevent loan fraud
The most common, widely used loan fraud detection method is identity verification testing, and let’s see how identity verification helps in reducing loan fraud.
Digital identity verification to lessen fraud attempts
Fraudsters exploit vulnerabilities in detection by compiling fake applications, or synthetic identities, that are a composite of several different identities. To prevent application fraud, financial institutions must successfully identify fraudulent activity or fraudulent identity documents in real-time at the beginning of the new account opening process. AI-powered ID verification is a great way to authenticate users at scale without sacrificing security while still gaining an edge on scammers.
Digital verification procedures that are part of a well-developed CIP and Know Your Customer (KYC) practices reduce the chance of synthetic identity fraud, while virtually eliminating doctored documents. Through consumer-permissioned (transactional and account-level information that a consumer gives a business permission to access on their behalf) access to financial data, verifications can be based on or validated by information direct from a financial institution. This is dramatically better than relying on documents that have changed hands at least twice in the loan application process.
AI- powered digital verification by Insight Consultants
Insight Consultants AI-Powered Document Verification Solution is a solution that was designed to give financial institutions an easy-to-use, compliant, secure, and cost-effective method of loan application fraud prevention. Artificial intelligence is able to solve the problem of balancing security and efficiency. AI-powered identity verification provides optimal fraud prevention and ensures highly effective authentication in compliance with KYC requirements. Using consumer-permissioning provides an additional layer of protection as it requires the applicant to know unique personal identifying information (PII) for each financial account they intend to use. As they go through the digital verification process, several aspects of an applicant’s identity are challenged. A fraudster would require access to PII to successfully launch a digital verification, which is nearly impossible.
In case you want to take your business security to next level, write to us at connect@insightconsultants.co. Else connect with us swiftly, fill out our request form here
Keep Reading
How is AI transforming fraud detection in the financial service industry?
The average thief isn’t on the streets or in train stations anymore; but is online. Due to its surge in popularity, and fast transaction cycles, online lending is a prime target for cybercriminals. Though financial institutions may have always had customer security in mind, the industry has felt the backlash in recent years.
Yet, as the saying goes, knowledge is power. You can make the best utilization of the convenience provided by technology aiming yourself with the power of knowledge. Cyber-attack methods and tools keep evolving with advancements in technology, increasing the possibility of ingenious scams that can be deployed from anywhere across the world.
Role of AI in combating fraud
In this scenario of increased cyberattack, AI mechanisms are emerging as the means to strengthen cybersecurity and thwart attacks. Research reveals that 63% of financial institutions believe that AI can prevent fraud, while 80% agree that AI plays a critical role in reducing fraudulent payments and attempts to commit fraud. Machine learning technology can be deployed across multiple channels (e.g. transactions, loan applications, etc.) in the financial industry. Banks and financial institutions can benefit from patterns that emerge with use of AI and ML to prevent frauds even before they happen.
Leading ways lenders are using AI for fraud detection
Building purchase profiles: To accurately detect fraud, financial institutions must first understand what typical customer behavior looks like. Using machine learning to sort through vast amounts of data from past financial and non-financial transactions, FIs can build and slot customers into several different profiles.
Developing fraud scores: All transactions can be assigned a fraud score by using data from past legitimate transactions, incidences of fraud and risk parameters set by the financial institution. The score, which considers variables such as transaction amount, time, card use frequency, IP address of a purchase, and much more, is used to assesses the fraud risk involved with that particular transaction.
Enhance underwriting: AI can have far-reaching benefits for underwriting performance. Increasingly accurate loss predictions enable underwriting teams to spot good and bad risks, grow a profitable portfolio, and automate processes to streamline their workflow.
Fraud investigation: Machine learning algorithms can analyze hundreds of thousands of transactions per second. Investigating and prosecuting fraud claims can be incredibly time-consuming, so ensuring agents are armed with the proper tools to increase efficiency is essential.
Know Your Customer (KYC): AI-backed KYC measures can verify ID and documentation, match fingerprints and even perform facial recognition almost instantaneously. This powerful tool strikes the right balance between customer security and convenience.
Digital organizations can identify automated and more complex fraud attempts faster and more accurately by combining supervised and unsupervised machine learning as part of a larger Artificial Intelligence (AI) fraud detection strategy. There is no question AI is making cybersecurity systems smarter. Whether this technology is used for securing authentication, threat detection or bot battling, AI and ML can prevent bad actors from infiltrating and manipulating company networks.
Insight Consultants fraud detection strategy using ML
To detect fraud,
- Machine learning model collects data
- The model analyzes all the data gathered, segments, and extracts the required features from it.
- The machine learning model receives training sets that teach it to predict the probability of fraud.
- Creates fraud detection machine learning models. This model detect fraud with high accuracy.
An outdated financial system is always full of loopholes tricksters can use. Luckily, machine learning has the potential to improve bank fraud detection with data analytics and help nearly every industry.
If you are looking for ways to harness the power of machine learning and AI for your business, or would just like to know more, Contact Us.
Get insights to stay ahead in lending industry
Insights delivered monthly !
