5 minutes reading time
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February 22, 2022

The risks of fraud during the lifecycle of a digital product

Fernando González Paulin
Fernando G. Paulin - CEO
Experto en la generación de valor a partir de datos con experiencia en el uso de inteligencia artificial en el sector financiero para resolver problemas complejos. Especializado en detección de fraude mediante el uso de datos.

Learn all about the risks of fraud during the life cycle of a digital product.

There is no doubt, the current panorama has accelerated the transformation and digitalization in all industries; but if we focus specifically on the financial sector, digitalization has gone from being an option to a necessity.

Few industries have as many challenges ahead as banking; adapting their current processes and operations to the online world (launching new products, technologies and channels that were not considered) has led to a complete digital revolution in the sector. Today it is possible to open an account 100% digitally without having to go to a local bank branch. But this business model change has also brought with it a major threat: digital fraud.

In 2019 according to CONDUSEF (National Commission for the Protection and Defense of Financial Services Users, the financial ombudsman), cyber fraud claims are as much as 13% of the complaints; by 2021 this fraud represented 33% of the total complaints, meaning that there was a steeped increase by 20% in only two years.

Criminals take any available opportunity to commit fraud; this crime can be carried out in many ways and at any time. Therefore, we must be alert throughout the entire customer's life journey (from acquisition, conversion, growth and retention to reactivation); constantly monitoring the customer journey map is essential to prevent and mitigate fraud in time.

The stages that we should consider monitoring, especially in a financial product are pre-origination, origination, maintenance and collection. In each of these stages, different types of fraud can be committed.

At the pre-origination stage, there is a high risk of suffering identity fraud; this occurs when someone uses another person's identity without their consent, and they do it to commit fraud or other crimes. This could be to apply for a loan or mortgage credit among others. According to the 2022 Identity Fraud Study: the virtual battleground by Javelin Strategy & Research, almost 42 million people in the U.S. were victims of identity fraud in 2021, which had an economic loss of 52 billion dollars.

First-party fraud is usually done in a deliberated and opportunistic way. This type of fraud is very difficult to detect during the onboarding process, as it requires transactional information to complement the analysis. The spectrum of activities related to this type of fraud is very broad since it can range from a person lying about their current situation to obtain some type of benefit related to goods, services, or money (without the slightest intention of paying), or even a criminal network that requests loans from different financial institutions knowing that the money will never be returned. According to the study The True Cost of Fraud in Latin America 2021 by LexisNexis, first-party fraud is among the top 3 fraud threats that financial institutions will face in the coming years.

In the maintenance stage, 2 types of fraud can happen: account takeover fraud and transaction fraud.

Account takeover fraud, or ATO, is when a fraudster gains access to an user's account credentials and takes control of that account to steal funds or information. The techniques to do so are quite diverse, ranging from phishing, malware, and man-in-the-middle attacks, among others. According to FICO's 2021 Digital Consumer Banking and Fraud study, a credit analytics company, 36% of the account holders stated that their greatest fear lies in being victims of bank account appropriation.

Regarding transactional fraud, we know that addressing, breaking down and detailing this type of fraud would take an entire article, so we are going to talk specifically about card-not-present fraud (CNP). This occurs when a criminal obtains the account number, expiration date and card verification code (mainly credit) and uses this information to make fraudulent purchases. Fraudsters can easily get stolen profiles through the dark web. This fraud can substantially affect the performance of a company, slowing down activities and solving what happened through expensive and exhaustive litigation processes, where often the company must cover the cost of the chargeback and the amount lost in the transaction.

If we talk about the collection stage, we can realize that the frauds mentioned before have a direct impact on the success of the collection process; if the aforementioned types of fraud have not been previously detected, they are often confused among delinquent customers, allowing fraudsters to repeat this criminal pattern over and over again without anyone detecting them. For this reason, it is crucial to have the real customer's information (previously verified) to give a correct follow-up.

Fraud risks at different stages of the life cycle of a digital product

Criminals evolve and use sophisticated strategies to commit different types of cyber frauds, according to Report to the Nations of the Association of Certified Fraud Examiners (ACFE), it is estimated that companies lose an average of 5% of their annual revenue because of fraud; fintech companies that lack reliable controls to prevent it will be more affected by the economic losses.

The positive side of all this, is that fraud prevention solutions companies are not far behind and are on the lookout for these criminals, implementing valuable tools such as artificial intelligence and machine learning, which help to detect patterns and behaviors that can be adapted in future applicants to avoid fraud and drastically reduce economic losses.

At Trully, we not only have solutions that incorporate these technologies but also cover the entire customer lifecycle to protect you and your users from scammers.

Face analysis combats identity theft fraud by analyzing and comparing the faces of your applicants with all the faces we have on our network.

ID analysis stops the use of stolen data by comparing your user's ID information with the information we have on our network.

Data enrichment increases the assertiveness of your machine learning models through the valuable information of your users and other data sources such as IP, email, device type, CURP, RFC, etc.

Decision maker helps you make the best decision to avoid identity theft fraud, automating the process and reducing the workload on validation desks.

If you want to know more, contact us. Trully, real solutions against fraud.

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