The incidence of cybercrime continues to grow worldwide. Crimes are increasingly committed in the digital space rather than in the real world. This trend is confirmed by Germany’s Federal Criminal Police Office (BKA), whose position paper entitled Cybercrime 2021 reported a 12 per cent increase in crime on the internet. However, experts believe that the real figure is significantly higher as not all crimes are reported. Nor has the financial sector been left untouched by this trend. Fraud offences, in particular, have been on the rise for years, coinciding with the advance of digitalisation in the banking sector. Therefore, banks must implement secure fraud prevention and detection measures to avoid financial risks and reputational damage. At the same time, banks and financial institutions must offer seamless customer journeys. In our latest blog post, we outline the difference between fraud prevention and fraud detection and reveal which methods have proven effective.
Fraud prevention and fraud detection: a brief comparison
Fraud prevention refers to all measures that banks and financial institutions can take to protect themselves in advance against fraudulent activities. These include implementing security measures and checks to reduce the likelihood of a successful fraud attempt. Among these are methods of identifying persons, monitoring accounts and reacting to possibly fraudulent activities. Typical security measures involve password protection, two or multi-factor authentication, or encryption. However, manual security measures are also used. For instance, there are specified banking policies, and training is offered to employees so that they are equipped to prevent fraud attempts.
As a result, fraud prevention processes cover all steps: from the first suspicion of fraud to the necessary fraud prevention steps and all the way to the procedural analysis.
Conversely, fraud detection describes the process of identifying fraud after it has taken place. This is generally done with the help of analysis tools that can track down data anomalies that point to the fraudulent behaviour of a person. For example, companies and banks can deploy specialised software tools and algorithms to analyse large volumes of data for irregularities. These processes often include machine learning (ML), data mining and algorithmic pattern recognition.
Whereas fraud prevention is proactive, fraud detection can be deployed both proactively as well as reactively. With proactive use, data is continuously monitored to detect and prevent anomalies. Reactive deployment is when previous fraud attempts are investigated and uncovered to identify the cause of the fraud.
By combining fraud prevention and fraud detection, companies can optimise their anti-fraud security measures and minimise the damage caused by fraudulent activities.
Fraud prevention and fraud detection in the financial sector
Given the level of fraudulent activity, it is extremely important for banks to use fraud prevention and fraud detection. After all, banks are a popular target for fraudsters.
If they are to retain and protect customers' trust, banks must ensure that their systems and processes are secure and robust. Implementing security measures such as multi-factor authentication (MFA), data encryption, and monitoring accounts and transactions are key components of fraud prevention.
At the same time, banks must be able to detect and prevent fraud attempts quickly – to minimise damage to their customers and their business. Technologies such as data analytics, artificial intelligence and machine learning play an important role in this regard. Banks can react quickly and detect potential fraud cases by monitoring transaction data and identifying unusual patterns.
Strong customer authentication also plays a key role in detecting and preventing fraud in online payment transactions. This security standard requires customers to confirm their identity by specifying at least two out of three factors. These factors can be something they know (e.g. a password), possess (e.g. a mobile device) and an inherent trait (e.g. a fingerprint). By utilising SCA measures, banks can ensure that customers are actually who they claim to be and that authorised persons carry out transactions.
SCA is, therefore, an important component of a comprehensive strategy for combating fraud and securing online payments.
Example of a typical online fraud attempt
Fraud attempts are particularly frequent with online payments because it is relatively easy for fraudsters to use stolen credit card data or other personal information to make payments. One typical form of online fraud is identity theft, which is when criminals steal the identity data of unsuspecting victims and use this data to make online transactions.
- Two or multi-factor authentication (2FA or MFA): two-factor authentication assigns one or multiple additional security layers to the customer to ensure that only the authorised customer can execute transactions. This is typically enabled with verification via SMS or email – or with the help of biometric identification such as fingerprints or facial recognition.
- Adaptive Authentication (Risk-based authentication): this involves evaluating the risk of the transaction and then applying a suitable authentication method on this basis. For example, a higher authentication level may be necessary if the transaction is unusually large or is made in an unusual location.
- Behavioural analysis: banks analyse the behaviour of their customers to detect patterns and anomalies that could suggest possible fraud. For instance, suspicious transaction patterns or unusual changes in user activity could indicate potential fraud risk.
- Artificial intelligence (AI) and machine learning: banks also utilise AI and machine learning to detect data patterns and identify suspicious activities.
By applying these measures and implementing SCA, banks and other financial service providers can help prevent online fraud and make online payments more secure.
