Use Case – Artificial Intelligence for Risk and Compliance

Use Case_Artificial Intelligence for Risk and Compliance

Artificial Intelligence is the superset of natural language processing, machine learning, pattern recognition, and more such sophisticated technologies. With its dozens of other applications, AI is proving itself very useful in risk and compliance too.

The Problem

With increasing digitization, every industry needs to process an excessive amount of data every day. This data could even be in terabytes. So, we either need automation or enormous workforce to handle the processes. Employing so many people for data processing and operations not only increases the financial burden on organizations, but also boosts the chances of errors and slows down the processes.

Performing risk and compliance tasks through non-AI methods or manually causes the delay in decision-making and action. Sometimes, advanced and bigger threats go unnoticed due to this.

The Solution

According to an IBM report, 50% to 90% of business tasks can be automated as of now. AI can be used for automating business operations so that risk analysis and compliance could be performed in parallel. From chatbots used in capturing the visitors or leads to detecting serious risks or probabilities of fraud – AI algorithms can be deployed for everything.

The major solutions, AI can provide in risk and compliance, are:

  • Data Classification

Classifying data with different labels and controlling the access to this data based on users’ rights helps safeguard it from unauthorized traffic. At the same time, it serves as a good basis of designing search algorithms and organizing the data. With the help of machine learning, NLP and AI, this task becomes easy and efficiently doable.

  • Fraud Detection

Fraudulence, internal and external, are indeed the biggest threat to organizations. For example, credit card frauds or fake transactions may cause financial losses to a company, if it goes undetected or uncaught. However, Artificial intelligence can prevent such incidences from happening by blocking the card when abnormal behavior is detected. In fact, as per a Deloitte report, 56% of companies in the insurance sector and 31% in the banking sector are already using AI for fraud detection.

  • Creation of Regulations and User Behavior Control/Analysis

AI lets the businesses automate their processes by defining the rules for acceptable behavior by users in the network. Thereby, such an arrangement prevents any risk-inducing activity. The system can detect malicious activities and report it before the damage is caused, if AI is utilizing properly.

  • Third-party screening

As organizations have to deal with multiple third-party tools and APIs, third-party screening becomes a necessary task for risk mitigation and threat prevention. AI can be deployed to limit the actions and data access for all third-parties. Different implementations can be put into place for different applications. Exiger has found that AI can give consistent and reliable results in the case of 3rd party and vendor screening.

  • Internal Risk mitigation

Internal threats, human defaulters and erroneous behavior can cause damage to organizations, hence are risks to be detected and mitigated on time. AI can monitor errors, transactions, trade operations, actions, adherence to regulations and more, in order to perform internal risk mitigation. In many sectors, 20-30% of Risk Management and Compliance operations are being handled using AI.

The Results

AI-driven risk and compliance systems can be used in improving the decision-making efficiency of organizations. It can notify the companies about potential risks in advance so that actions could be taken in time.

Different user behavioral analysis methods help the business units in detecting abnormal activities in real-time. Hence, organization data remains more secured. AI has reduced the frauds and increased the changes of fraud detection even if the mishap has occurred.

Automation and involvement of AI reduce the chances of human errors and risks associated with these errors too. Ensuring better information flow, it is also giving better control over operations and data to the organizations now. Overall, it has lowered the risks to manifolds by better data organization, anomaly detection and improved access control mechanism.

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