As scammers, fraudsters, and money launderers are getting more complicated with their scamming tactics; the Fraud Monitoring Platform is getting more aware and smarter with their fraud prevention tactics. As our financial institutes live through a riskier world full of frauds and scams, it has become essential to stay aware of such risks. However, the numbers of rising frauds are threatening. As per a survey, it is determined that:

Frauds are persistent: Survey says that more than 49% of the global financial institutes have been victims of online frauds and financial scams. The numbers represent only the detected crime rate. The actual crime rate is still not figured.

Fraud prevention methods are costly: Most financial institutes reported spending more on fraud investigations and related interventions than they lost to the frauds.

Using fraud detection platforms after the damage is done wastes time and irritates clients: Survey reported that most financial institutes noticed that their Fraud Detection Platforms were reporting fake     positives.

Fortuitously, the progress of fraud prevention technologies has offered financial organizations more secured and trustworthy techniques that are helpful in fighting frauds and financial crimes. Let’s have a look at these defensive strategies that can strengthen up us against financial criminals.

1.    Using Artificial Intelligence to bring more efficiency and accuracy to fraud prevention and detection

 Machine learning in AI is a stronghold that helps in both accuracy and efficiency in fraud detection. Whereas supervised machine learning algorithms can self-learn from their targets within the data, flagging everything that is not as observed norms, then applying collected data to the unseen latest data. Unsupervised machine learning is helpful to detect unsupervised potential risks. The method works without any given targets, so it searches for abnormalities in data.

Machine learning decreases fake positives from current approaches while identifying previously unsupervised risks. For example, launching an online payment model that represents rapid success for real-time fraud detection. It will detect 50 percent of fraud, alerting only 0.5 percent of the portfolio, with a meager chance of fake positives.

2.    Congregate frauds, anti-money laundering, and cyber events

Many financial organizations are making use of substantial data architectures to combine data across classic remote functions. However, the activity will only make sense when these functions are integrated into a more holistic view of risks.

With the increase of similarity between data, regulations can identify risk closer to real-time. Also, it brings plentiful opportunities to reduce operational costs and increase the efficiency of cross-functional view development.

 Real-time monitoring of transactions has become a basic need for each type of payment now. It incorporates not only financial transactions but also authentication, location, sessions, and device event data.

As per a top-tier global bank report, the adoption of 100% real-time decisioning helped them reduce fraud. It also helped them increase their card revenue by fifty million dollars as it offered low fake positives and better consumer interfaces.

3.    Rationalize investigations with intelligent case management

Immediate adoption of AI concentrates on the automation of manual processes, so it can be beneficial in cost reduction for running a Fraud Monitoring Platform. An advanced intelligent case management system is capable of:

Prioritizing cases, offering investigating steps, and quick-tracking primary cases.

Showing details of associated clients, accounts, and beneficiaries as alerts.

Smartly search and gather data for cases from internal databases or third-party providers.

Representation of data in a self-explanatory manner related to the activity under evaluation.

Allowing automated prioritization of strategies on client contacts.

In the summary