Nowadays, companies strive to develop information collection systems that better understand their customers. Yet, obtaining and storing data requires the buildout of processes that protect people’s privacy and security from fraud, theft and data misuse. In this context, alternative scoring models are a great mechanism for detecting applicants fraudulent personal information, which is extremely useful for the financial industry.  

As technology advances, malicious programs can burst into companies’ security systems and hack users’ identities. There are different types of cyberattacks, and some of the most harmful for the financial system are:  

• DDoS (Distributed Denial of Service): The intention is for servers to collapse, creating a traffic avalanche by overloading the infrastructure so that legit users can’t access it. Bots and computers are employed, making users access malicious pages driving huge disconnection losses for the companies.
• Point of sale attacks: Attacks diverting transactions or reading data from debit or credit cards.
• Watering hole: Attackers enter a malicious code, infecting sites frequently visited by one or more of these victims.
• Identification of vulnerabilities: Attackers investigate company systems to find weak points to carry out an attack.
• Phishing: Attacks directed to the company’s client portfolio. Criminals pretend to be the person to obtain private data such as passwords, bank account numbers, among others.
• Ransomware: A malware that prevents users from accessing their personal files and asks for a payment in return for the account retrieval.

The financial sector is a very tempting industry for hackers when programming an attack due to the high volumes of assets that it moves. According to a study performed by Sophos, a world leader in cybersecurity, during 2021, 34% of financial services organisations were attacked by ransomware. Additionally, 25% of these organisations which had their data encrypted paid to get their information back. As a result, an average of 2.10 million dollars was spent to restore damage caused by these attacks.

New alternative credit scoring systems are a great solution for minimising malicious attacks. These advanced scoring methods allow companies to get to know their prospects better and avoid being victims of fraud. Alternative data consists of information from different sources such as internet shopping, mouse movements, social media behaviour, rent payment, apps and invoices and more. These are all collected and processed by artificial intelligence algorithms to construct client behaviour models and obtain more detailed credit profiles.  

Additionally, alternative information is processed as metadata, protecting clients’ identities. The collected data is encrypted and non-PII data, “no personal identifiable information”, to avoid the attackers pinpointing the user. 

Cyberattack risks and customer fraud are on the rise. Therefore, a safe data collecting system is crucial for any online business, especially within the financial world. Furthermore, new intelligent products, such as alternative credit scoring, are acquiring even greater importance in this modern world as an efficient option to anticipate a client’s behaviour thoroughly and, thus, their risk. 

Check out this article on how credolab can help companies reduce the risks associated with fraud without affecting the customer experience and journey.