Online banking didn’t eliminate the opportunities for thieves. Moreover, it even helped the criminals do their deeds. Armed bank robberies still happen and crooks have new doors to break in: now they use hacking and social engineering to steal your money.
This is why it’s essential for financial institutions to constantly monitor user activities and access channels in order to prevent frauds and other risks.
Lending companies have an additional problem: they want to know how risky it is to lend money to their potential clients, especially those who don’t have a credit history. Such businesses should employ risk management solutions, which, among other things,
check every available source — bank accounts, insurance policies, credit card statements, big data and so on — for a reliable information on customer’s financial status. As a result, a lender minimizes chances of bad credit, thus securing its business outcomes.
One of such solutions is Kontomatik’s Financial Health Indicator (FHI). It works on top of Kontomatik’s Banking API and uses the data from client’s bank account to immediately evaluate the credibility of a customer. It then returns the value from 0 to 100
explaining how healthy the user’s bank account is – with the larger the number, the healthier the account. This output is based on average monthly income, total balance, bank account reliability (how old and rich in data the accounts are), the sum of loans
granted by other lenders, and much more.
Big Help From Big Data
A bit different risk management tool comes from Lenddo — its LenddoScore also gives an indicator of customer’s credibility, although based not on his or her bank account information. Instead, it relies exclusively on non-traditional data derived from a customer’s
social data and online behavior. Lenddo’s score is a kind of a predictor of an individual’s character or 'willingness to pay' and helps to discriminate between good and bad borrowers.
Leveraging of non-bank account data is a foundation for Big Data Scoring solution. This system gathers pieces of data from a variety of online sources — such as social media, Google search keywords, IP address and device used — on the loan applicant and
links it with their behavior on a lender’s website. Big Data Scoring can improve the predictive quality of an in-house scoring by up to 26%.
Risk management solutions are not only for lenders: Forex brokers can benefit from LXRisk offered by Leverate. It gives a complete view of exposure and broker revenue in a simplified user interface that enables brokers to view total exposure and manage execution
strategies, thereby maximizing their risk management abilities.
Fraud Lives On
One of the cyber fraud and risk management specialists listed five main fraud risks organizations and their clients could encounter this year. They include account takeover and identity theft, which are responsible for $16-billion worth of losses in the
U.S. alone. And every new successful attack on sites, where sensitive user data is stored (e.g. credit card or social security numbers), means more opportunities for hackers to pretend to be customers and turn this information into real money.
Social engineering is perhaps the oldest and the most effective kind of attack and is very hard to avoid, as hackers are more and more creative in cheating their victims. They target the weakest link in every system: humans. And it’s not only customers who
are in danger, receiving spoofed emails or phone calls – employees in financial institutions are the ideal people to exploit since they can be used to open the doors to internal systems and data of an organization and its clients.
Another threat comes from fraud rings. These are crime syndicates, which specialize in some kind of fraud and attack potential targets in a very organized way. They are harder to detect and track than other types of fraud.
New Technologies = New Risks
With mobile banking becoming the primary bank access method, no wonder it quickly drew the attention of crooks as a target of attacks. Authorization codes takeover, infiltration of data stored on a smart device, malware used to intercept information exchange
between apps and servers – hackers try to exploit all vulnerabilities or weak links related to mobile access or payments.
And last but not least, leveraging of social media in banking and other financial services – as a tool to enhance the user experience, not for communication purposes – poses another risk. There are lots of malware apps on Facebook for example, and hackers
will surely try to make other ones targeted at users of social media enhancements for the financial industry. Takeovers of accounts in social networks will also be more profitable.
To Detect And Protect
Are financial institutions helpless when faced with the fraud risks mentioned above? Absolutely not. With the exception of social engineering attacks, where client and employee awareness is the key (and therefore requires education and training), there are
software tools available to eliminate or mitigate the risks and/or their effects. These applications are complex pieces of code, which need to be integrated with the backend banking systems to constantly monitor for suspicious activities in the environment.
Solutions like Bottomline’s Cyber Fraud and Risk Management Platform, NYCE’s Risk Management, GlobalVision’s Guardian Officer, or VeraFin’s FRAMLx were developed specially for detecting potentially fraudulent operations and minimizing risks. They can help
large organizations such as banks not only to secure their business but also fight crimes.