WHAT IS FRAUD ANALYTICS?
Fraud analytics uses Big Data techniques to prevent online financial fraud
It can detect and mitigate fraudulent activity while it is taking place. It can also predict future behavior and suggest preventive methods.
With machine learning, all financial transactions, from start to finish, can be examined for potential risk. It starts with collecting and analyzing behavioral, device, and transactional data.
For example:
- Predictive analytics examines patterns for future fraud potential via unexpected events.
- Visual analytics can monitor transactions for suspicious activity through dataset diagrams and dashboards.
- Forensic analysis can examine the reasons for a fraud event and the relationship between factors causing it.
These techniques can be applied across all types of financial fraud: identity theft, credit card fraud, fake claims, embezzlement, etc. They protect consumer funds and enhance the reputation of the firm.
THE BENEFITS OF FRAUD ANALYTICS
For fintech enterprises, there are several benefits of deploying fraud analytics
- With automation, all available transactions can be scanned for possible red flags.
- Data from different sources can be unified for precise analysis.
- The financial dimensions of anticipated fraud can be accurately forecasted.
- Automated fraud-detection systems can reduce dependence on human resources and be cost-effective.
- Machine learning systems enhance existing fraud prevention tools for better outcomes.
- Fraud analytics increases the speed of fraud detection. Remedial measures can be taken as soon as possible.
- Lessons from analytics tools can be applied for improved security protocols.
Before AI and machine learning, financial companies used a rule-based approach to check fraud
For example, transactions over a specific size or those occurring in unusual locations needed extra verification.
Such rules were laid out after analyzing past patterns. They also relied on fraud detection scenarios by algorithms.
Rule-based systems are straightforward. They add and adjust procedures manually. Often, they do not make use of all the data available.
However, they remain essential. Rules can catch many apparent cases of fraudulent behavior.
On the other hand, machine learning systems can quickly process large amounts of data. They can identify correlations to predict the likelihood of fraud. The chances of employee error are reduced. Decision-making becomes simpler.
Because machine learning systems work in real-time, they can quickly minimize the impact of fraud. Verification measures can be diminished, and detection can be automatic.
Rule-based systems can also miss new types of fraudulent activity. With predictive machine learning based on continuous streams of data, machine learning systems can spot old and new fraudulent schemes.
THE FUTURE OF FRAUD ANALYTICS
As applications and algorithms become more sophisticated, more value can be derived from data and existing technology
AI-driven behavioral analytics will further identify suspicious patterns across activities. When the algorithmic process becomes more visible and understandable, employees will engage with it in more efficient and valuable ways.
The relevant data will also increase as more customers shift to digital for their financial transactions, further heightening the efficacy of machine learning to prevent fraud.
For fintech companies, the road ahead is filled with prospects and challenges. AI can be their reliable partner in this journey.
