Role Of AI In Risk Management – Applications And Challenges/ai-insights/role-of-ai-in-risk-management-applications-and-challenges

Role Of AI In Risk Management – Applications And Challenges

February 10, 2022

Role Of AI In Risk Management – Applications And Challenges

Artificial Intelligence (AI) is here to stay. AI education has an imperative role to play now than ever before. Every day businesses find more activities than can be optimized, thanks to the efficiency and effectiveness of this evolving technology. Within marketing, customer service, and even security, the power of information management and AI has advanced the standards for companies in a highly competitive environment. Broadly speaking, Artificial Intelligence adapts to meet users’ needs by analyzing usage patterns among various data sources or general guidelines within a sea of information. Here, it’s critical to clarify & divulge the fact whether AI is a game-changer in Risk management or otherwise? AI is actually changing the game one step at a time. Banks and FinTech companies are implementing risk management systems with AI solutions to facilitate decision-making processes, reduce credit risks and provide financial services tailored to their users through automation and ML algorithms. AI’s ability to analyze large data relevant for cyber security, risk management, risk assessment, and accurate business decision-making is tremendous.  


Unfortunately, the benefits won’t come without risks. When implementing AI technologies, companies must pay special attention to their associated challenges such as data protection along with the costs of implementation. The following are the ways we can implement AI models to reduce AI risk and take advantage of these tools for the organization. 

    First and foremost, identify the organization’s regulatory and reputational risks. Conduct a risk assessment, based on current frameworks and the company’s organizational values. Use it to determine the data you need to collect and how you want to process the same. 

    Based on previous risk assessments, it’s possible to define which data sets are suitable for AI model processing and which ones aren’t. Choosing the right data sets influences the quality of the results massively. 

    Once, we have the user data, we proceed to build a useful model. Consider the level of transparency you want in AI operations, review regulatory limits if any on how AI can be used for certain business processes. 

    Like other risk management tools, the use of AI must be constantly evaluated and adjusted. It’s critical to consider the dynamic needs of the organization and the possible drawbacks that this technology may present. 

Some specific USE CASES that have benefited from AI integrated risk management systems include: 

    ML engines can analyze large amounts of data from various sources that can generate real-time prediction models that allow risk managers & security teams to address risks well in time. The models are fundamental to developing early warning systems that assure uninterrupted operation of the organization & protection of its stakeholders.  
    AI provides the ability to evaluate unstructured data about risky behaviors in the organization’s operations. AI algorithms can identify patterns of behavior related to past incidents and transpose them as risk predictors.  
    This traditionally requires intense analysis processes for financial institutions and insurers. AI systems can substantially decrease the workload of these processes and reduce fraud threats by using ML models that focus on text mining, social media analysis, and database searches.  
    AI tools can also process and classify all available information according to previously defined patterns and categories and monitor access to these data sets. 

Despite these advantages, there are two main CHALLENGES of AI in Risk Management procedures and practices: 

  • FINANCIAL ASPECT- Even with cloud-native services, processing huge amounts of data may be costly. Enabling specialized AI services will be highly expensive. 
  • PRIVACY- With AI and machine intelligence, many in the security sector are concerned about Data privacy. Data protection measures such as Encryption, Transit Security, Tokenization, and Obfuscation may be required for data companies that upload to cloud services. 

While the major cloud storage services provide data controls, specialized AI and ML services such as Amazon SageMaker; Amazon Rekognition, which uses AI to extract and analyze images and videos; and Google Cloud AI change this dramatically. As not all services can utilize the same encryption key management and usage methods & restrictions that businesses have in place, data security may be compromised. Apart from the services in use, the location of sensitive data utilized in ML and AI operations is a key regulatory and compliance concern. 


The state of AI in risk management is well-understood by delving deep into the operational defects of the ecosystem. Risk management teams will continue to gain from the quick analytics processing of big data sets as cloud-based AI and ML services become more prevalent, reducing the constraints of more manual risk management and risk analysis procedures in the past. Aside from the technical obstacles of developing AI apps for banking, such as designing proper and appropriate algorithms, there are also regulatory and data access rights to consider. The FinTech industry is majorly controlled by a set of data-related laws that must be followed to the letter. Data breaches cost expensively and new law such as the GDPR in the European Union places a severe obligation on firms that handle personal data. 


As the risk in the financial landscape is rapidly increasing, it takes a Herculean effort to stay on top of growing fraud risks, credit risks, and fast regulatory changes. Artificial Intelligence in risk management can help detect fraud and credit risk with greater precision and scale by augmenting human intelligence with extensive analytics and pattern prediction skills. In the tech world, AI-powered analytics solutions may dramatically speed-up compliance procedures while also lowering expenses. 

Artificial intelligence and risk management perfectly align, when there is a need for handling and evaluating unstructured data. It is estimated that risk managers of financial institutions will focus on analytics and stopping losses in a proactive manner based on AI findings, rather than spending time managing the risks inherent in the operational processes.