Financial service providers like banks have relied on Artificial Intelligence (AI) and machine learning to provide services like conversational banking and credit underwriting. One area, above all, that has found the most use of AI in the industry is related to better fraud management and prevention. During the last few years, there have been too many fraud attacks costing businesses millions worldwide.
AI-adoption in the finance sector
Cybercriminals are constantly evolving and finding new ways of attack and destruction. The traditional rule-based methods, which focus on potential fraud identification based on pre-coded rules, are no longer effective and completely redundant. These methods can no longer defend or prevent modern, complex frauds. It means the finance sector needs a defensive mechanism that is effective and dynamic.
This is why AI and machine learning are so vital today.
There has been a significant implementation of AI, specifically machine learning, to counter these attacks and secure the individual business sphere. Banks and financial institutions have enthusiastically employed these techs as a direct reply to ever-growing sophisticated cyberattacks. And given the delicacy of the situation, the AI-adoption rate has also gone up in the last two years.
AI implementation
Before moving ahead, we need to understand the difference between Artificial Intelligence and machine learning.
AI is a broad concept that involves computers replicating human cognitive functions. On the other hand, machine learning is a part of AI that relies on data analysis methods through which machines can learn from data. They analyze and use the data to make decisions without human intervention. AI algorithms can analyze millions of data points quickly to detect unique fraud behavior patterns.
To implement effective AI and machine learning plots in the business, financial institutions need to address the following factors:
- Use of a mix between supervised and unsupervised machine learning
Supervised models can tag data as fraud or non-fraud for the computer to ascertain legit or illegitimate patterns. Unsupervised models use a self-learning method that groups data points to fill gaps when there is very small or no tagged data.
- Employing larger data sets
Machine learning keeps improving its accuracy as and when it learns from a large amount of data. To become better in fraud detection, a computer can build its understanding from millions of data points. This helps the system to better differentiate between what is fraud and what is not.
- Specialized fraud analysis
Generic behavior models cannot work well in specific fields like finance. While they might detect some anomalies, only specialized models can detect sophisticated threats and less obvious fraud patterns. These models rely on fraud-specific predictive characteristics and highly advanced profiling to cater to particular fields.
Concluding note
With time, more and more business and financial institutions are adopting AI technologies into their system. AI techs like identity verification assist banks in detecting and ban suspected fraud profiles. KYC is currently a well-known security method being used by financial institutions around the world. And companies like iDenfy are eagerly developing advanced identity verification tech to counter cyber security threats worldwide. You can find out more on idenfy.com.
