Fraud detection in fintech: how to detect and prevent frauds in the lending industry?

TrustCheckr
Jan 22 · 4 min read

Over the years, the fintech industry has evolved splendidly. There has been an incredible amount of growth the industry has seen over the years with artificial intelligence and machine learning giving rise to an assortment of financial transactions online. This has led to an astonishing increase in businesses who specialize in financial transactions online and an alarming increase in online fraud which has now proven to be a breakneck to every business and customer who engages in online transactions. As the advancements in the industry grew, the fraudsters in the net also enhanced their strategies in committing frauds and scamming business organizations, hence looting millions of dollars. The most common types of online frauds we see today are phishing or spoofing, identity fraud, account fraud, transaction fraud, well the list goes on.

Spoofing or phishing is the malicious act of disguising an unknown scam network as a known and trusted network. Spoofing is done in order to get access to a victim’s personal information or an organization’s computer networks. Spoofing can cause an individual to lose important personal information and for organizations, it is an opportunity for cyber attacks and network infections.

Synthetic identity fraud is a type of fraud where scammers create new identities by combining fake data with stolen original data. Scammers steal data from users like addresses, IDs, phone numbers etc. through social networks and incorporate it with fake data. This gives rise to a believable type of fake data which shows no identifiable victim and is often undetected and very dangerous to business organizations.

Account frauds are another type of unique frauds in the fintech world. Scammers use the technique of account fraud to loot large amounts of money from banks and then disappear after stealing the money. This type of scammers usually has a good credit score maintained for a long time and then at once take loans of large amounts of loans and disappear which usually leads to bad debts in the lending company and eventually losses.

Transaction frauds are another dangerous type of frauds which when serious can cause hefty losses to the business. Transaction frauds occur by scammers using stolen credit cards or identities in order to make large purchases and pay for. The minute transaction time required for the payments usually gives very less time for the business to verify the authenticity of the user. When the victim reports the loss of money in their account, the fraud is detected and the company ends up paying the compensation to the victim, the scammer usually going undetected.

The most common fraud we see in the lending part of the fintech industry today is the counterfeiting of personal information by fraudsters. Today, it is very easy for fraudsters to counterfeit others’ personal information like IDs, photographs, phone numbers and addresses. For scammers, getting such personal information is as easy as finding it from social networks which host most of customer’s vital and vulnerable data. What makes it ever more favorable to scammers is the fact that fintech businesses work towards quick processing of the loan applications because everyone wants their money as soon as possible. This gives lenders very less time to assess their clients’ applications and makes it easier for scammers to commit their frauds.

Scammers most commonly commit their frauds by applying for loans with a lot of fabricated information like spelling errors or unrealistic information related to incomes, all of this being stolen identities. When the businesses, somehow lend the loans, it turns out that they have been scammed and end up facing huge losses. The development of machine learning and advanced data analytics has proven to be quite successful in combating online frauds and controlling the loss of money by fintech businesses. This is usually done by using various APIs to look up customer’s history of relationships with banks and credits and determine their scope for lending, whether they would be consistent enough to pay back their debts, if they are genuine or fraudulent. This way of preventing fraud is very expensive and time consuming.

The other way to detecting and preventing fraud is by grading the applicant’s potential of turning out to be a scammer. This was is more scientific and economical. It works by building a standard scale of measurement of the creditworthiness and Genuity of the applicant. Scoring models are special algorithms which calculate the creditworthiness and Genuity of the applicant. These models compare the results of the applicant with the standard scale and determine whether the applicant is worthy of getting a loan or not. This type of advanced data analytics has proven to reduce fraud by a large extent and also has reduced costs. TrustCheckr’s set of creditworthiness calculating APIs are an elite and successful set of fraud detectors today.

Check out www.trustcheckr.com for more information about the same.

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