With any great change comes a great risk. This statement is particularly true in the current digital age, where consumers enjoy accelerated service delivery, open access to information, and facilitated mundane processes, while service providers race to meet their expectations as well as to enable safe environments.
Around 61% of consumers had to change their payment methods or reduce their interactions with digital payment because of scams and cyber fraud. Such patterns are concerning for banking and financial organizations as their ability to provide necessary protection is put to test. In order to prove their ability to protect customer data, they must secure integrity and provide the expected safety levels. However, the massive volume of data, the intensity of processes, and the sheer variety of scenarios require non-stop commitment to monitoring and identifying risks.
This is where AI makes its entrance, augmenting human agility with its robust analytical and predictive capabilities and enabling a unique personalized approach for each enterprise. In this detailed guide, we'll provide a comprehensive breakdown of such an approach by exploring the use cases, benefits, and specifics of artificial intelligence in risk management.
AI and risk management: analyzing the perfect match
Understanding the role of AI in managing risks requires a deep look into risk management, its goals, processes, and key challenges.
Risk management: objective and purposes
Risk management's main task is identifying and controlling risks to the enterprise's earnings and budget. These risks vary from financial and legal to situational and strategic and can be triggered by many factors.
The uncertainty of the business landscape, a force-majeure situation, and human error—are only a few of the many sources of risks that emerge. Accordingly, risk management consists of several key processes that encompass a great variety of scenarios and activities that can potentially harm an organization.
- Fraud detection
Fraud detection is an irreplaceable component of every loss prevention strategy. Directed at preventing persons from acquiring property or money through illegal means such as identity theft, false insurance claims, or company funds embezzlement, it includes analysis of suspicious behaviors, data matching, and building probability models that enable early identification of fraud activities. While all enterprises aren't immune to fraudsters, this risk management process is particularly relevant in such areas as banking, insurance, and government services. - Risk identification
Crucial to businesses of all types and sizes, this process is centered around determining potential risks to the enterprise. Within risk identification, the main internal and external threats are outlined and documented in great detail, providing leaders and executives with a clear picture of where the danger might come from and what kind of damage it can cause. Risk identification involves stating risks, doing brief and in-depth identification, external and internal cross-checks, and, ultimately, a risk statement. Such a routine is repeated for cataloging every new emerging threat. - Risk assessment
Risk assessment covers the evaluation of all potential business hazards that can have a negative impact on the organization's reputation and income. It encompasses a wide range of risk types (from natural disasters to business competition to sudden regulatory changes) and includes risk profile creation, as well as identifying how each risk type can impact a particular enterprise asset. - Risk analysis
Risk analysis aims to evaluate the probability of a certain risk occurring and affecting the enterprise and calculate its outcome. Often practiced by healthcare organizations, banking institutions, government agencies, and nonprofit establishments, risk analysis is crucial for decision-making and undertaking new endeavors as it enables enterprises to assess all the pros and cons of their choice and map potential long-lasting consequences of taking a risk. - Risk mitigation
Risk mitigation is directed at minimizing the negative effects of threats encountered by organizations. All the information received during risk identification, assessment, and analysis is used to create detailed plans to address dangerous situations and alleviate them before they can cause damage. Accordingly, risk mitigation involves developing a set of actions, steps, and guidelines for executives and employees to undertake, securing certainty even in the event of disruption. - Risk monitoring
Risk monitoring covers businesses' need for awareness of potential risks and their effects. This activity prioritizes continuous review of existing threats as well as the existing risk contingency plans—to ensure their relevance and efficiency. Risk monitoring is integral to securing enterprises' resilience and ability to respond to threats swiftly. It keeps risk managers updated on the threats they might face and the best practices for dealing with them.
Key risk management challenges and their effect on strategies
With the main goals and processes of risk management outlined, there is also a point in mentioning the most impactful risk management limitations that affect end results and reduce effectiveness.
Lack of high-quality data
Insufficient amounts of historical data negatively influence risk assessment and risk analysis, leading to inaccurate statements and improper estimation of danger.
Unaddressed complexity
Enterprises need to map out the relationships between different risk types for more responsive planning. Failure to acknowledge the interconnectedness of risks and how enabling one threat triggers another results in companies’ inability to prevent cascading effects.
Low predictive capacity
Risk management plans work when there is certainty. Therefore, vulnerability to unexpected risks and new threats can reveal weak points and make a previously efficient strategy unable to mitigate the danger.
Cognitive biases
Human error and bias frequently influence risk management strategies. Certain human perspectives, such as overcautious behavior or, vice versa, unnecessary optimism, can result in ineffective strategies or low enterprise productivity.
Zero integration
Risk management strategies are often executed separately from the enterprise’s operational or financial activities, preventing risk managers from getting the full picture of opportunities and the efficiency of their practices.
No long-term vision
Enterprises often focus on short-term risks and don’t analyze the long-lasting impact on their strategy and enterprise, which removes flexibility from their planning.
In addition to the challenges mentioned above, a more specific limitation should be considered. The fact that every industry and niche has a rather individual perspective increases the difficulty of risk management. What is qualified as a risk in one area isn't considered a risk in another—and vice versa. Therefore, any effective risk management and prevention strategy should always consider specific qualifiers. However, doing so takes a very dedicated and detail-oriented approach, necessitating augmenting human vision with technological precision.
AI in risk management: impact and benefits
Since enterprises exist in very dynamic and competitive environments, the limitations of traditional risk management models become even more apparent.
New risk factors make themselves known with each global disruption, any social shift adds another variable to an already complex relationship between different risk types, and there is more data than analytical tools and teams can handle.
Using artificial intelligence in risk management allowed enterprises to close capacity gaps, surpassing their limitations and gleaning the following advantages
- Robust unstructured data processing
Business environments are surrounded by volumes of external and internal data, from large, structured data sets to unstructured data sources (images, webpages, emails, and business documents). Making sense of such information requires capacity beyond traditional analytics. For that reason, enterprises leverage data mining, machine learning, and natural language processing (NLP), arming themselves with smart tools that can dissect previously unavailable gray data areas and glean insights for proactive risk identification and fraud detection. - Accelerated risk assessment
AI risk management software can automate such mundane processes as documentation and segmentation of risk profiles, reducing the workload of risk managers and letting them concentrate on more strategic goals, such as in-depth risk analysis and generation of risk mitigation strategies. Liberated human resources and AI's ability to process millions of data points within minutes also enable a more proactive decision-making environment where human bias is replaced with detailed, relevant, and meticulously calculated insights. - AI-powered threat forecasting
Harnessing the potential of AI and machine learning for risk management enabled organizations to future-proof their strategies with predictive capacities by analyzing their historical data and detecting hidden patterns and factors as well as their effect on performance. For example, ML models can be trained to monitor certain risk indicators and predict the period within which these indicators will reach critical levels—providing organizations with greater visibility on risk triggers.
- Personalized risk management
Since utilizing AI in risk management releases trapped data value by turning unstructured data sets into organized and comprehensive pools of insights, it creates opportunities for flexible and personalized risk strategies that consider particular types of industry-specific risks. Additionally, AI’s capacity of interacting with enterprise data and using it for analytics and forecasting provides a valuable contribution to agility of risk prevention models.
The importance of establishing an AI risk management framework for an enterprise is directly related to the importance of building enterprise resilience. In a world of possibilities and uncertainties, AI becomes a game-changer and a vaccine against pitfalls and threats. With its deep comprehension of data, powerful analytical capacity, and forecasting ability, it inoculates businesses against potential threats, injecting greater knowledge and sharper awareness into their risk management planning.
How to address risk management in AI adoption
Regardless of the promise demonstrated by AI as a technology for preventing risks, 2024 became the year when enterprises had to acknowledge the darker side of the matter: AI’s ability to create more risks.
Every technology has vulnerabilities which can be exploited. AI isn’t an exception. However, the problem is, we know about the weak spots of more conventional technologies because their adoption and evolution pace was more gradual, allowing professionals to learn about all the specifics and address the weaknesses accordingly. However, with the emergence of AI vendors and adopters are in a rush to move to the next level, so they don’t stop to evaluate the environment and consider all the risks. This is, probably, the most dangerous and challenging aspect of adopting AI.
According to the research by MIT CSAIL and MIT FutureTech, at least 30% of the risks are overlooked by the existing AI risk frameworks. In total, there are 43 such frameworks, from which the researcher identified up to 700 risks posed by AI technology. Following their conclusions, there are several most common risky scenarios that can be caused by artificial intelligence.
76%
AI system safety, failures and limitations
73%
Socioeconomic and environmental harms
71%
Discrimination and toxicity
68%
Privacy and security
68%
Malicious actors and misuse
However, what are the reasons for artificial intelligence going from a helper to a potential danger?
- Black-box reasoning
The reasoning behind the algorithms that AI uses for making conclusions and responding have always been hard to explain. Instead of following clear commands, this opaque functionality, also known as “black-box reasoning” operates based on data and subtext learned. Due to this, an AI system can give false replies or make conclusions that do not always make sense to users and developers. The latter is less than desirable, especially for healthcare facilities that consider adopting artificial intelligence.
The unexplainability of AI is often described as its special feature and unique attribute. However, adopters and users who want to use AI for accurate and non-biased results don’t find such an approach reassuring. Technology should be explainable and predictable, so before vendors proceed to developing new models of AI, they need to dedicate time and effort to securing AI explainability
- Automation bias
Automation bias is more of a psychological issue than technological. The more advanced solutions become, the more comfortable people grow with trusting their judgement and conclusions — to the point of not fact-checking the results. AI, in particular, is an outstanding case of automation bias. It has been entrusted with court filing preparation, scientific papers, and providing medical advice – and provided wrong answers or incorrect data in each of these cases, which led to consequences ranging from lawsuits to reputational damage to loss of money on a solution that failed to work as intended.
It’s a common problem: workers expect too much from AI and their expectations impact their trust in the technology. Since artificial intelligence is often promoted as a workplace assistant, enterprises assume it can make the same decisions humans do. This mindset is counterproductive. AI is a tool — it can assist with tasks, such as processing data or replicating simple, mundane activities that don’t require critical thinking, but it can’t work on more complex projects and deliver the results of the same quality and accuracy. Moreover, like any tool, AI has it’s flaws. So, it’s critically important to understand where AI’s capabilities end – and where human talent must shine.
- Biased data
The quality of AI’s performance is directly related to the quality of data used to train it. Whenever AI is accused of discrimination or bias, the problem stems from the information: it could be biased or insufficient for the AI to make proper conclusions and decisions. For example, it wasn’t uncommon for AI computer vision programs to discriminate against people based on their appearance and gender: an Amazon AI hiring program showed clear bias towards hiring male employees because of the company’s limited historical data on female engineers and computer scientists. It wasn’t Amazon’s intention to show bias: but since the company’s data lacked the versatility Amazon wanted to see in their hiring, leading to such a controversial outcome.
It’s a myth that AI can’t show bias. People have a tendency to be biased — and AI learns from people. So, it’s not enough to adopt an AI system and expect it to drive accurate information and make right decisions on its own. It takes curated data and robust knowledge of an enterprise's goals and future objectives in order to create and implement a solution that works as intended.
Despite its generally optimistic outlook on AI, the World Economic Forum considers it a considerable threat in its Global Risk Report for 2024. The lack of proper risk regulation and the AI’s ability to distort information can potentially be weaponized by malicious actors as part of cyberwarfare and misinformation campaigns. Due to this, the organization calls for a more responsible approach to the technology and stricter AI regulations.
Does it mean that AI adoption doesn’t improve risk management but, in fact, complicates it? Quite the opposite. Simply rejecting AI as a technology doesn’t solve the problem: aside from malicious actors ready to exploit artificial intelligence to their advantage, refusing to explore AI means staging oblivious to the potential benefits and complications. Additionally, AI can assist security experts with instantly detecting AI-powered phishing scams and hacking attacks.
The term “AI arms race” is often used to describe Big Tech companies competing for the excellence of their AI products. However, it’s more applicable to the ever-going standoff between organizations and cybercriminals. The latter waste no time in weaponizing artificial intelligence, using it for data theft, damaging data infrastructure, and paralyzing the work of enterprises. Accordingly, enterprises must keep up, learning how AI works, how to use it and how to counter malicious activity
The key to safely mastering AI and avoiding data bias, reputational damage, and other kinds of negative scenarios is in accountability. Today, it’s evident that before enterprises adopt AI, they need to establish a system of rules, guidelines, and practices that ensure that the technology is used fairly, all data and privacy protection are in place, and all the processes are transparent.
Artificial intelligence in risk management: use cases
After establishing the most obvious game-changing qualities of AI risk management, it makes sense to elaborate on its less evident and yet immensely valuable advantage—the enablement of personalized risk strategies.
The fact that every industry and niche has a rather individual perspective increases the difficulty of risk management. What is qualified as a risk in one area isn't considered a risk in another—and vice versa. Therefore, any effective risk management and prevention strategy should always consider specific qualifiers. With AI augmenting the work of human professionals and traditional data analytics, enterprises become empowered to build strategies for accurately addressing and flawlessly preventing risks most typical for their niche.
For example, AI enables image recognition, which allows the identification of patterns and the categorization of visuals. In some industries, it's merely a convenient feature that accelerates work and optimizes tasks.
However, in healthcare, it's a solid risk management tool as it minimizes the risk of misdiagnosis by accurately identifying pathologies and symptoms on X-ray scans, mammalogy screenings, or MRT scans. As a result, it saves patients' lives and prevents risks to the hospital's capital since misdiagnosis costs the US health system nearly $100 billion per year.
The versatility injected into risk management by AI can be further illustrated by the breakdown of the most prominent use cases across industries.
AI risk management in Finance
With nearly 83% of financial organization employees seeing AI as the cornerstone of their future success, financial institutions remain among the earliest and the most active adopters of AI risk management software. The continuous investment in technology made it possible to revolutionize a number of highly relevant areas, such as:
- Fraudulent activity tracking
Fraud still remains a glaring issue in BFSI. In 2023, the financial loss caused by cyber fraud reached $486.5 billion worldwide. To keep up with the challenge and prevent financial losses, BFSI companies invest in AI and machine learning for risk management, equipping their fraud detection arsenal with ML models.
For instance, banking institutions have been using credit card transaction data to train ML models that would then use this data to identify suspicious patterns indicating fraudulent transactions. Additionally, AI algorithms prevent card cracking, detecting bot behavior and instantly blocking it.
- Credit scoring
With the need for stronger, more accurate, and more complex credit risk models, financial institutions constantly look for ways to safeguard themselves against high lending stakes. Their search empowered their risk management with AI credit scoring that provided a more detailed evaluation of a client's creditworthiness by analyzing a large variety of data points. Compared to traditional credit scoring that uses only historical data, AI-powered credit is capable of going through diverse datasets, providing the most realistic estimations for each specific client and offering more certainty to credit risk prediction. - Rogue trading prevention
The case of Société Générale became a cautionary tale of how unauthorized trading can cost a financial organization its capital, its clients' trust, and even its place on the market. Due to this, investment banking organizations became particularly aware of risk management measures that provided them with greater visibility into trader behavior. AI platforms contributed a lot to protecting systems from rogue trading by enabling in-depth analysis of each trader's portfolio, check-in/check-out times, calendar slots, call times, and email traffic and then used that data for predictive models that estimated the risk of misconduct.
AI risk management in Tax Services
The 90% increase in tax e-payments indicates the productive results delivered by the proactivity of tax service providers and successful adoption of technologies such as AI and machine learning for risk management. This positive dynamic signifies the smooth comprehensive digital transformation of taxation processes also known as Tax Administration 3.0.. However, since tax service providers face the consistent demand for transparency and control, they need to continuously improve their set of tools and methods for enabling process visibility and preventing risks.
- Tax evasion prevention
Taking measures to reduce the risk of tax evasion is detrimental to the safety of government finances and public services. For that reason, tax service providers continuously work on their strategies for closing tax gaps and removing loopholes. The fraud detection capabilities of AI and machine learning play a major role in dealing with tax fraud as they make it possible to develop accurate tax fraud identification models that analyze taxpayer behavior, detect anomalies, and predict the probability of fraud. Within such AI risk management frameworks, tax agencies gain more clarity and certainty when investigating tax fraud and how it was committed.
Other evasion prevention strategies include addressing unintentional tax evasion due to human error. Within this risk management strategy, tax service providers assist taxpayers with paying their taxes by providing them with personalized, high-quality service and improving communication. For instance, using intelligent chatbots enabled tax agencies to successfully keep taxpayers informed about changes in laws and regulations while gathering their feedback and relevant information and identifying the risk areas. - Enhanced regulatory compliance
Lack of tax standardization has always been a great issue in tax management, resulting in a large variety of filing deadlines and tax structures depending on the region. Such complications make the work of tax accountants operating in multinational companies particularly difficult as they have to ensure full compliance and avoid fines while staying fully up-to-date with the massive amount of varying regulations. Failure to navigate the tricky landscape of tax regulations ends in costly penalties that can amount to billions of dollars.
Deploying AI solutions for improved compliance helps organizations systemize the complex structure of regulations and standardize tax management across all regions.
By automating a number of routine flows, AI-powered risk management takes a lot off tax accountants' shoulders. For instance, AI solutions accelerate financial data analysis and tax liability calculation, providing teams with relevant insights within seconds. They also enable fast compliance report generation and smooth regulatory change monitoring, complete with regional requirements, which helps tax professionals keep up with regulations and avoid the risk of penalties.
- Operation enablement
Following the Tax Administration 3.0. vision, around 73% of tax professionals prioritize achieving greater accuracy and efficiency in tax reporting. Accomplishing this goal requires facilitating complex internal processes and reducing the paperwork burden for employees, taking the risk of human error out of the equation. With the help of AI and machine learning for risk management, tax professionals can improve their detection of irregularities and anomalies, which would allow for more productive resource allocation, generally improving the operational efficiency of an entire enterprise. Additionally, the use of AI removes the risk of data inaccuracies and inconsistencies, which is crucial for the complex structures of modern tax agencies that use different systems and apps.
AI risk management in Healthcare
The relationship between healthcare providers and AI can be described as complicated. While the value of incorporating AI platforms into such healthcare areas as inventory management and patient care is obvious, the process of incorporating AI-based tools into the workflow remains rather slow. For instance, only 58% out of 393 AI-powered platforms for augmenting radiology processes became commercially available. Such hesitation is commonly associated with such concerns as a lack of employees with the skills necessary for managing AI-based tools and the need for new approaches to data management. Tackling these challenges is only possible within long-term strategies based on collaboration between all the involved stakeholders.
However, the growing demand for AI tools in the healthcare market paints a rather optimistic future for artificial intelligence in healthcare. Given that the market size of AI solutions for healthcare is estimated to reach over $173 billion by 2030, there is a promise that facilities will overcome their limitations in order to optimize their performance and quality of services with the help of AI.
Concerning the subject of AI risk management in healthcare, it’s worth noting the technology’s ability to identify and prevent threats to something more valuable than capital—patient health.
- Improved patient safety
According to a recent study, 1 in 4 patients admitted to the hospital suffers from an adverse event. Such events include injuries from taking the wrong medication or contraindicated drugs assigned without knowing the patient's full medical history, undergoing wrong surgical procedures, and caregivers' failure to assess risks connected with each individual patient. In turn, adverse events are often triggered by the lack or unavailability of data and human error. Using AI risk management software makes it possible to negate these two factors by injecting more accuracy and personalization into diagnosing and patient care.
For professionals who operate in intense conditions and are expected to make correct decisions at all times, AI can make a huge difference. Take AI computer vision, for example. It can be used in patient face recognition, improving identification and preventing medication dispensing errors. Applying AI to surgical guidance provides surgeons with the data and visibility they need to enable a controlled environment during operations and prepare a plan of action for potential outcomes.
In other words, AI not just makes more data visible, it also puts the most important information right in front of doctors and caregivers, turning each medical history into a transparent and trackable journey.
- Enhanced cardiac health monitoring
Monitoring the health of patients outside healthcare facilities has always been a challenge. However, such issues as technology obsolescence and the lack of communication between monitoring watchers and hospital personnel majorly contributed to the growing risk rates, revealing the need for a new approach.
Equipping telemetry monitoring with risk management AI offers a solution to this challenge, closing problem arasand expanding the potential of health monitoring with predictive power. For example, it is possible to train ML models to calculate the risk of the patient's cardiac arrest based on their medical history, genetic markers, cholesterol levels, and daily activities. Such information can then be automatically generated into reports and sent over directly to the frontline teams, securing regular feedback, proper preparation, and timely response to emergencies - Fraud risks reduction
The risk of fraud isn't exclusive to BFSI sectors only. Healthcare organizations can have their critical business process, known as account payable (AP), affected by instances of fraud, such as unauthorized payments to employees or vendors, payments to non-existent vendors, or diversion of payments to vendors through tapering with vendor data.
In 2024, 80% of organizations experienced attempted or successful AP fraud attempt. Given that annually a successful fraud costs companies 5% of their revenue, this rate is alarming. The reason why AP frauds keep posing an issue can be attributed to several factors.
First of all, scammers keep growing more sophisticated and elaborate in their methods. Second, enterprises have a rigid structure due to their size, which complicates their approach to detecting suspicious activity. As a result, AP fraud cases can remain undetected for almost an entire year.AI risk management nips such schemes in the bud by providing a comprehensive analysis of AP transactions and vendor payments with suspicious patterns (payments associated with purchase orders, multiple vendors receiving payments through the same bank account, vendors getting added to the file before payment and removed after the payment is made).
How to build a cross-enterprise AI risk management framework?
The potential of AI in risk management lies in its ability to bring order into the chaos of variables and data that enterprises come across when they plan to improve their resilience. However, tapping into this ability and maximizing the value it can drive requires more than an open-minded mindset. There are several steps executives need to undertake before they can commit to building a solid AI risk management framework for their organization.
- Define risksAs it was mentioned before, what makes AI and risk management work so well together is that enterprises can focus on monitoring and preventing very specific risk types. However, it also means that enterprises should be able to determine their top risks and threats and provide detailed information.
In AI model risk management, the quality of data is everything. Vague or irrelevant information would lead to inaccurate results, rendering the entire model useless. So, to get results that matter, you need to assess your understanding of risks and see how they align with the vision of stakeholders from your departments. The more you know about the risks and their sources, the easier it will be for you to communicate your goals and needs to data scientists responsible for training your AI risk management model.
- Review strategies
It's important to remember that artificial intelligence can't build risk prevention strategies on its own. While free of human bias, AI is also not powered by the human mind's flexibility and decision-making—it only enriches it with accurate analytics and data processing. So, executives shouldn't expect artificial intelligence to suggest new, actionable risk management plans. Instead, they should think about how AI can supplement and improve their risk management strategies by identifying current weak spots and checking how they can be alleviated with the help of technology. - Manage the risks of AI
Since implementing AI for risk management comes with its own risks, enterprises need to invest their time and resources into proper legacy structure, which indicates the necessity for thorough design planning, collaboration with their legal teams, and rethinking enterprise governance through the lens of managing AI. This process is part and parcel of the AI Risk and Security Management framework (AI TRiSM), which will become a staple for organizations wishing to evolve digitally and contribute to accountable use of innovation. - Collaborate with professionals
Trying to build an AI risk management model without previous expertise increases the risk of a less-than-satisfactory experience with the end product. To avoid this, executives should see that their resilience-boosting effort is handled by experienced data scientists and AI engineers who can enrich each step of the development process with their domain knowledge while ensuring full synergy with the specific enterprise needs, current AI regulations and future-proof practices.
If you’re looking for a committed and vetted professional team to enhance your risk management strategies with AI, let’s chat! As a trusted digital partner of Fortune 100 insutry leaders, we bring unparalleled commitment, professional integrity, and agility into realizing our clients’ innovative resilience-building concepts into industry-defining AI solutions. With our qualified ML engineers, business analysts, and data scientists, you’ll see your vision take shape and transform into the perfect tool for enhancing your business immunity.
