How is AI Changing Risk Management?
Artificial intelligence and machine learning have proved beneficial for many sectors, including risk management and data security use cases. Advancing technologies have widened the attack surfaces. Therefore, insurance companies, banks, asset or property managers, and other players in the industry should reconsider their risk management strategy.
Artificial intelligence effortlessly augments manual risk management strategies through innovative analytical abilities. The ability of AI to analyze massive amounts of structured and unstructured data improves risk management capabilities and compliance. With this, businesses can identify risks timely and effectively and make informed decisions.
Below are ways in which AI is changing the face of risk management.
Mitigation of Information Security Risks
Data storage and other general information security risks are major challenges for all businesses. Banks, insurance companies, and other businesses store a lot of sensitive customers and corporate information. Hackers and cybercriminals often target vulnerabilities in the networks and servers of such businesses.
Successful data breaches expose sensitive customer data, putting the customers and company at risk. Unfortunately, traditional risk management solutions couldn’t mitigate the growing risks effectively for several reasons. For instance, cybercriminals regularly upgraded their techniques and tools, making it impossible for traditional security solutions to prevent these risks.
Fortunately, machine learning and artificial intelligence help businesses mitigate privacy and security risks. Unlike traditional cybersecurity solutions, businesses can use AI and ML to gather valuable insights from large data sets. AI systems enhance risk management by;
- AI detects data patterns relevant to previous attacks
- AI-automated processes can generate an updated inventory of a business’ IT assets
- Artificial intelligence and machine learning can analyze vast amounts of data and gather insights on emerging threats. These technologies also identify possible vulnerabilities that cybercriminals can exploit.
- Data risk managers can use ML and AI tools to evaluate the strength and weaknesses of the company’s network.
Fraud Detection
Artificial intelligence and machine learning also boost the fraud detection abilities of companies. Fraudulent transactions are a major challenge affecting the growth of ecommerce businesses. Ecommerce merchants, financial service providers, and banks often end up paying for deficiencies caused by fraudulent transactions. Customers also lose confidence in the ecommerce retailer and the financial service provider.
Unfortunately, traditional technology has several shortcomings in fraud detection and mitigation. There is a lot of data in the operational environment that can be used to detect possible fraud. Financial institutions and ecommerce companies handle massive volumes of transactions.
Ideally, businesses should find sufficient clues about fraudulent transactions from these data sources. Unfortunately, affording the human effort required for such analysis is impossible. However, AI systems make fraud detection easy. Modern technologies use advanced techniques to detect fraud. Moreover, this includes;
- Supervised and unsupervised machine learning algorithms – supervised machine learning algorithms use already labeled data of fraudulent and legitimate financial transactions. Once trained with labeled data, ML systems can easily identify frauds.
- Using ML for behavioral analytics – machine learning systems also compile behavioral analytics. They can analyze customer habits and behavior using various aspects, such as password resets, average expenditure rate, specific requests for duplicate cards, change of address, and more. The system can then build a profile from this data to reduce false positives.
What are the Challenges of Using AI in Risk Management?
While using AI for risk management is overly beneficial, enterprises face two major drawbacks when integrating AI into their risk management practices. They include;
- Financial challenges – rolling out AI infrastructure and processing vast datasets is expensive.
- Data privacy issues – introduction of stringent data compliance and regulation measures can limit the use of AI. Before companies upload their data to AI-based cloud services, they should fulfill all data protection measures, such as transit security, encryption, and tokenization.
Bottom Line
In conclusion, risk management gains a lot from quick big data analytics as artificial intelligence and machine learning increasingly become prevalent. However, enterprises should be ready to handle the technical obstacles of AI, such as building AI apps that adhere to data protection regulations.
