How Financial Companies Use AI and Data Analytics
Today, Artificial Intelligence plays a vital role in the operations of banks and other finance companies. Technology has enhanced personalization, insights, and customer service.
As Brett King, the financial services influencer, says, “The best advice and service in financial services happens in real-time and is based on customer behaviour, using principles of Big Data, mobility and gamification.”
To begin with, machine learning and automation improve the efficiency of fintech employees. Data can be analyzed to yield precious insights.
These advances have helped fintech companies make significant progress. Yet another area in which AI and data analytics have significantly contributed is fraud detection and prevention.
The repercussions and costs of a data breach have reached an all-time high. Among the causes are: compromised credentials, phishing and cloud misconfiguration. That’s not all. There's been a corresponding consistent rise in banking scams - here the greatest offenders are identity theft, and cyber-attacks.
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.
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.
Fraud analytics uses Big Data techniques to detect and mitigate fraudulent activity while it is taking place.
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
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.
Machine Learning Systems Versus Rule-based Systems
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.