4 Ways AI in Risk Management Will Change Everything

Are you wondering how to use AI in risk management? You’ve come to the right place. In this blog post, I am going to show how AI can help with risk management.

In this article

1. Managing information security risks: a key use case of AI in risk management
2. Using AI and ML to manage and reduce enterprise risks
3. Using AI risk management solutions for fraud detection
4. AI in risk management at enterprises: better data classification
5. Frequently Asked Questions on AI in risk management

In 2024, the global AI market size reached $184 billion. The greatest use of AI in product making was in the risk industry, according to a report.

AI in risk management can make a positive difference in the following ways:

1. Managing information security risks: a key use case of AI in risk management

Managing information security poses challenges to every business. Artificial Intelligence (AI) and Machine Learning (ML) can help enterprises mitigate data privacy and security risks.

Challenges that organizations face when trying to protect their confidential data and sensitive information

Businesses of all kinds store plenty of sensitive data. This data includes confidential corporate information as well as sensitive customer data.

Cyber-criminals and hackers routinely target the IT network and servers of businesses to steal this data. They aren’t “lone wolves” any longer. Cyber-criminals are highly organized. As Statista reports, cybercrime imposes heavy costs.

In some cases, employees within organizations flout data ethics. These factors put confidential and sensitive data at risk.

Data breaches expose corporate confidential information and sensitive customer data. These put companies and customers at risk. These also represent reputational risks for businesses.

The severity and frequency of data breaches made regulators harden their stance too. Many industries must comply with stringent information security and privacy regulations. The banking industry, financial services industry, and healthcare industry are examples.

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Businesses operating in these sectors typically run high regulations-related compliance costs too. In summary, securing confidential and sensitive data involves high risk.

Companies need to contend with the following generic cybersecurity challenges too:

• A large attack surface;
• A growing number of attack vectors;
• A large number of devices to be secured in every company;
• Too few knowledgeable information security professionals.
  

Why traditional technology solutions can’t meet some of the new challenges concerning information security

While traditional information security solutions do a great job, they also have a few limitations. These are as follows:

• Cyber-criminals continuously upgrade their tools and techniques. Traditional information security solutions tend to prevent known cybersecurity risks. They might not mitigate new risks.

• Enterprises need to be proactive as far as information security risk oversight is concerned. Therefore, they need to know about the potential risks concerning cybersecurity. Traditional information security solutions might find such risk identification very hard.

• Identifying potential cybersecurity risks requires a thorough scanning of the environment. A lot of information might exist in unstructured data. However, traditional information security solutions can’t derive insights from such data.

• Companies need to spend a considerable manual effort to analyze various indicators to identify potential information security risks. They find it hard to allocate the necessary manpower for this.

How AI technologies and ML models can help companies mitigate some of the new risks in the information security space

AI systems can collect a vast amount of data from the operating environment of a business. Companies can use machine learning models to gather actionable insights from these vast data sets quickly. AI systems can enhance cybersecurity in the following ways:

• AI and ML systems detect patterns that are relevant to the attack surface of a company.
• Businesses can use AI-based automated processes to get a complete and up-to-date inventory of their IT assets by criticality.
• AI and ML systems can analyze large amounts of relevant data to gather insights about emerging cybersecurity threats. This helps the senior management of the company to understand what their information security vulnerabilities are. AI and ML systems also identify the vulnerabilities that hackers are likely to exploit.
• Information security risk managers in your company can use AI and ML tools to find out the strengths and weaknesses of your cybersecurity solutions.
• AI and ML solutions can identify the vulnerable IT assets in your organization.
• You can use AI initiatives and ML systems to improve the information security incident response processes and tools.

Note: Google, IBM, and Juniper Networks are just some of the companies that use AI and ML systems to bolster their cybersecurity posture.

2. Using AI and ML to manage and reduce enterprise risks

Managing and reducing enterprise risk isn’t easy, however, AI/ML can help.

Why do businesses find risk management hard?

Risk management is complex due to the following reasons:

• Uncertainties in markets are often tied to global events and trends. Even experts find it hard to predict many of them.

• Companies face information security challenges all the time. While many of them are external, quite a few of these challenges are internal.

• The infrastructural, technical, and procedural complexity of a business increases as it grows. Risk management in such a business becomes more complex since risk managers must manage many “moving parts”.

• Decisions related to matters like Investment strategies and insurance coverage are inherently complex. Naturally, risk management for such decisions tends to be complex.

The limitations of traditional technology solutions in enterprise risk management

Traditional technology solutions for risk management can derive insights from structured data. They can use these insights for risk management. However, they can’t process unstructured data.

For any company, there’s plenty of unstructured data in and around their operating environment. Plenty of insights is hidden in this unstructured data. They can’t gather these insights using their traditional risk management software products though.

How AI and ML help in risk management

AI can derive insights from unstructured data. Companies need to make some preparatory steps before they can process unstructured data. These steps are as follows:

• Storing data in a systematic and scalable manner;
• Organizing data;
• “Cleaning” unstructured data.

They can then use AI tools to understand this data. These tools could be text analysis tools. They might use ML and Natural Language Processing (NLP) capabilities.

Enterprises can use AI and ML tools for various risk-management related purposes, e.g.:

• Analyzing their risk appetite;
  
• Improving the risk management model interpretability;
  

• Stress testing;

• Back-testing.

Note: Citibank is an example of companies using AI for risk reduction.

3. Using AI risk management solutions for fraud detection

AI and ML can help organizations improve their fraud detection capabilities. In turn, this helps them to offer a better customer experience.

Fraudulent transactions pose a major challenge to the growth of digital services

e-Commerce is growing rapidly, however, fraudulent transactions are growing too. e-Commerce merchants, banks, and financial services firms face the brunt of this.

Companies often need to pay for the losses when there’s a fraudulent transaction. Customers often lose confidence in a financial services institution or e-Commerce retailer in the case of fraud. Therefore, fraudulent transactions pose a reputational risk too.

The limitations of traditional technology solutions in fraud detection

Plenty of data exists in your operational environment that can help you to detect fraud. Financial institutions process a massive volume of transaction data. Not just the banking industry, but e-Commerce companies also process a large volume of transaction data.

Theoretically, business stakeholders can find enough clues in these data sources to identify patterns of fraudulent transactions. The practical scenario is different though. You can hardly afford the human effort needed for this massive analysis.

Is AI-powered fraud detection easy? Not quite!

You can use third-party applications for fraud detection. These could use AI, however, fraud detection involves additional risks. These are as follows:

• The patterns of fraudulent transactions change. The ML model accuracy will reduce in that case.

• Typically, a small percentage of customers indulge in fraudulent practices. ML models for fraud detection might have imbalances due to them. You need model validation, which isn’t easy.

• Another fraud detection ML model risk is the lack of “explainability”. In this case, an ML model doesn’t explain why it flags a transaction as a fraudulent one.

• It takes time to build ML models without potential biases for fraud detection.

In summary, early efforts to use AI in fraud detection were undoubtedly innovative approaches. However, they had some limitations.

How AI and ML can improve your fraud detection capabilities

Modern solutions to implement AI in risk management use more advanced techniques for fraud detection. They are as follows:

Using supervised and unsupervised ML algorithms together

Supervised ML algorithms use “labeled” data. This includes fraudulent and legitimate transactions. Training with “labeled” data helps the ML system to identify frauds. Companies use unsupervised ML algorithms when “labeled” data isn’t available.

Using ML for behavioral analytics

Machine Learning models can help in behavioral analytics. AI systems using these models can analyze customer behavior and habits. They analyze many aspects, e.g.:

• When a customer conducts transactions;
• The average rate of expenditure;
• Changes of address;
• Requests for duplicate cards;

• Password resets.

AI systems build profiles using this, which helps to reduce “false positives”. ML algorithms help banks and financial institutions model their risk management framework aided by better profiles and customer intelligence. This helps to mitigate reputational risks and uphold ethical principles.

Detecting frauds more effectively by building ML models for risk management with large data sets

Financial institutions can feed better quality of data to Machine Learning models for fraud detection, furthermore, they can feed vast data sets. This helps ML models to identify frauds better. Such a successful implementation will result in lower operational costs too.

Detecting frauds better with the help of self-learning AI and adaptive analytics

Cybercriminals continuously find new ways to execute fraudulent transactions, and the patterns of these transactions change. Machine learning models used for fraud detection need to keep pace with these changes.

Such ML models typically use a threshold. They score transactions in reference to that threshold, where transactions above the threshold are investigated.

However, cybercriminals device new ways to dupe consumers. There might be fraudulent transactions right below the threshold. On the other hand, legitimate transactions can be slightly above the threshold.

Financial services firms have fraud detection specialists that investigate transactions. Many financial institutions incorporate the lessons learned by these specialists into their ML models. This helps them to utilize self-learning AI and predictive analytics, which improves their fraud detection capabilities.

Note: Visa is one of the several companies that use AI and ML for fraud detection.

4. AI in risk management at enterprises: better data classification

Classification of data is important for risk management. Organizations often find data classification hard, however, AI can help.

Why is data classification important for risk management?

Many factors make data classification important for risk management. These are as follows:

• Organize data for easier retrieval;
• Storing data systematically;
• Making data accessible to relevant business stakeholders;
• Using the right security model based on the sensitivity of data;
• Complying with regulations;
• Extracting insights from data.
  

The challenges of data classification using traditional technology solutions

Using traditional technology solutions for data classification poses challenges. This is due to the inherent complexities of data classification.

You need to keep in mind the sensitivity of data. This can be low, medium, or high. Data classification isn’t a one-size-fits-all activity either. It can be content-based, context-based, or user-based. Traditional technology solutions can’t quite keep up with these complexities.

AI and ML help companies to improve risk management with better data classification

Companies can use AI and ML solutions for large-scale data classification. Machine learning significantly helps with tagging data. By training ML models with tagged data sets, companies can classify data more efficiently. In turn, this gives them better data for risk management.

Note: SkySync utilizes AI for classifying data.

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Frequently Asked Questions on AI in risk management