Data and analytics have always been integral for successful risk management. However, they were left to the mercy of dedicated specialists until AI risk management stepped in to change the game. Applying AI-powered risk management is what risk managers do to generate risk predictions faster and error-free in 2022.
In this post, we’ll have a deep look into the AI risk management framework and how machine intelligence can prevent organizational threats.
What are the main drivers of AI in risk management?
Manual risk assessment has long been an industry standard. Thus, instead of threat prevention, managers would focus on managing business well-being and controlling financial recovery. Traditional assessment methods neither provided insight into risk factors nor mitigated uncertainty.
Conversely, AI-driven risk management allows for accurate threat analysis and can avert emergencies long before their appearance. This revolution was prompted by a number of factors that surfaced over the last few years.
Automation and Big data
The growing focus on automation, Big Data, analytics, and innovation is the main enabler of AI for risk management. According to IDC, new data creation will grow at a CAGR of 23% from 2020 to 2025, resulting in 175ZB of data creation by 2025. It means that both structured and unstructured data will burst forth, thus posing an analytical challenge. This amount of information necessitates agile AI solutions to support the digital ecosystem and process troves of data.
As customers are becoming more demanding, companies are vying to win the competition. The pandemic has also left a stamp on customer experiences, thus making them harder to follow. The great experience shift resulted in organizations unlocking state-of-art services. AI in banking risk management, for example, facilitates more secure financial services for clients.
End-to-end digital transformation
Finally, global digitalization ministers the adoption of the latest technologies. Digital transformation is gaining traction with AI and analytics leading the innovation. By 2023, digitally transformed companies are projected to contribute over half of the global GDP.
Along with next-gen technologies, digital transformation also accounts for a profound transformation of products and services, and organizational structure, including threat prediction.
What does risk management include?
Before we assess the synergy of AI and risk management, let’s see what this process typically refers to. Risk management is a consistent procedure that aims to detect, analyze and assess exposures. A deep understanding of potential threats allows companies to occupy a proactive stance to minimize the aftermath.
Risks also differ by the fallout area. Thus, companies can be affected by:
- Financial losses – these can be incurred through credit, market, or legal risks.
- Operational risks – loss resulting from ineffective business processes, systems, or external factors.
- Perimeter threats – material losses that affect financial companies.
- Strategic risks – are the direct result of ineffective decision-making.
Regardless of the type, each threat is identified through the same framework. Thus, intelligent software and managers follow the steps listed below:
- Identification – threats are detected and prioritized by importance;
- Measurement – employees assess the probability of a loss occurring due to those threats;
- Mitigation – exposures are minimized through insurances, asset sales, or diversification;
- Reporting – employees produce risk reports for the executives and parties involved;
- Governance – companies assign roles, responsibilities, and mitigation activities for a successful threat management strategy.
Despite the variety of threat statuses, artificial intelligence risk management can step in during each state and automate any activity related to the threat management framework.
Why use AI and machine learning for risk management?
Artificial intelligence and machine learning offer risk managers new opportunities to identify and manage critical exposures. As these technologies evolve, they are becoming better at understanding natural language and images, making it possible to analyze unstructured data. Additionally, machine-learning algorithms can be trained to identify patterns in data that human risk managers might miss.
Therefore, companies leverage artificial intelligence for risk management to reap strategic benefits and stay proactive against emerging business threats.
An unrivaled ability of smart systems to analyze huge amounts of data is the main benefit of risk management with AI. Given that about 90% of data collected today is unstructured, integrating cognitive analytics can put firms on the cutting edge. Leaders who employ cognitive technologies to foresee and proactively manage risk can gain a competitive advantage and use risk to propel their businesses forward.
Automated assessment and measuring processes reduce the need of hiring additional labor. Moreover, the human factor has a lower presence which boosts the accuracy of management. Once the company has all the necessary data, the algorithm takes over the tedious analyzing tasks.
Real-time and predictive analysis
By using ML algorithms, AI engineers develop models that can predict future risks with a high degree of accuracy. Companies can use these models to protect their customers and their businesses, both in real-time and in the future. As a result, organizations stay aware of new exposures based on historical data and have a faster response time in critical situations.
Most importantly, AI model risk management eliminates strategic risks that stem from erroneous business decisions. Since AI analyzes troves of data automatically, top management has a deeper understanding of all business processes. The 360-degree awareness results in better and data-driven decisions that protect the company internally and externally.
Richer data segmentation
The ability to adequately meet loss management requirements depends on sound segmentation practices. ML algorithms yield supreme segmentation and granularity as they can automatically take into account lots of data attributes. Granular segmentation also leads to higher accuracy and explanatory power.
How is AI used in risk management?
According to Chartis Research and TCS, the maturity state of AI in financial risk management is quite high globally. Thus, high-maturity institutions are located in the Western markets of Europe and North America. Asia-Pacific regions are behind with a 39.1% of maturity rate.
As for specific risk management AI technologies, machine learning algorithms, graph analytics, and neural networks are the most popular assessment techniques.
One of the most important steps in artificial intelligence is the ability to efficiently segment your data. Properly segmented data will allow you to apply the appropriate machine learning algorithms to critical values, which will result in better threat predictions.
In particular, machine learning segmentation can divide customers into categories based on their risk level. This approach helps businesses allocate resources appropriately and make decisions about which customers to focus on when it comes to fraud prevention and security.
Segmentation also helps supply chain companies classify their supply base by a supplier’s role and importance in the supply chain. This, in turn, leads to better anticipation and disruption prevention. Suppliers can also be divided by performance, location-related instability, and other factors that take a toll on a company’s wellbeing.
Neural nets are a powerful intelligent technique that develops complex relationships between input and output data. It means that this AI branch excels at uncovering hidden patterns and can assess and predict threats in ways that other models cannot. Also, neural networks can factor in the relationships between multiple risk areas and calculate the risk score based on a slew of factors. Credit risks and organizational vulnerabilities are most often evaluated by this technique.
Over the last few years, the business world has also embraced AI-amplified graph analysis. This blend is fundamental to anti-fraud and other threat prevention where complex patterns hamper traditional evaluation techniques. Graph analysis also supports real-time threat monitoring and models the relationships between individual objects as a graph.
However, this isn’t an exhaustive list of all smart techniques. Scenario comparison, Robotic Process Automation, and others have been also used successfully to analyze business vulnerabilities.
AI for risk management: top applications
There are many use cases where businesses of all sizes and verticals embrace AI and machine learning in risk management. Although AI in banking risk management is most well-known, the smart potential can safeguard other industries.
Malicious actions and frauds are one of the main reasons for huge losses. According to PwC, 46% of organizations suffered from fraud, corruption, or other economic crimes in 2020. The pandemic also made businesses more vulnerable. Thus, 39% of companies experienced a YoY increase in fraud during 2020. External factors are deemed the main perpetrators and account for 43% of all fraud resources.
Artificial intelligence risk management can significantly bring down the number of threats and cyber-attacks. Intelligent algorithms can be employed to analyze vast data sets of previous transactions or operations and uncover fraud trends. These insights can subsequently be used to detect and avert fraudsters in real-time.
Along with AI in financial risk management, smart fraud detection can be applied to the following vulnerable surfaces:
- Email phishing – computers distinguish between real and spam addresses;
- Identity theft – ML algorithms guard accesses to critical systems;
- Document forgery – neural networks distinguish between a fraudulent and authentic identity;
- Biometric authentication – facial recognition prevents unauthorized access to facilities, systems, and services.
The anti-fraud engine can reduce fraudulent transactions by up to 40% on top of existing AI fraud prevention measures, for the benefit of banks, merchants, and cardholders, as well as society in general. Also, the implementation rate for this use case grew to 14% percent, according to a McKinsey report.
Credit risk prediction
AI in credit risk management is another successful demonstration of intelligent loss prevention. This AI in financial risk management application evaluates whether the borrower can repay the loan. When the loan recipient has no ability to repay, it inherently implies credit risks. Credit ratings, in turn, can significantly lower the possibility of a default and, therefore, avoid financial losses.
So, how does AI in credit risk management work? Typically, it comes down to a smart credit score system. Scoring is a borrower assessment system that allows financial organizations to predict whether the loan will be reimbursed. The prediction is based on an analysis model.
Within this frame, fintech solutions segment customers by similar spending patterns, demographics, and other criteria. In this case, the model compares potential borrowers with previous debtors. The largest potential of an AI-based scoring system is its ability to include hidden factors in the analysis.
Unlike traditional credit scores, algorithms can figure out luxury spending or excessive purchasing. Also, alternative data such as phone metadata enables lenders to assess a borrower’s risk easier. Ultimately, increased analytical power can result in more accessibility for around 1.7 billion of the unbanked population.
AI-powered risk management is also applicable to compliance management. According to Mordor Intelligence, compliance liability is one of the top two risk categories that 57% of senior executives feel least equipped to face. The pandemic, endless oceans of data, and ever-evolving regulations have also made compliance a slippery slope.
That said, regulatory compliance documents have intrinsically been challenging to dig through due to their naturally complex text structure. That’s why traditional text analytics gave into natural language processing. This smart technology can process large volumes of regulatory data quickly and accurately.
NLP models sift through dunes of regulatory text and output summarized reports. The latter include salient rules and restrictions that companies need to follow for compliance.
Moreover, natural language processing can filter unstructured data and tag unstructured digital documents. This allows compliance managers and companies to establish more comprehensive regulatory practices and compliance strategies.
Employee workforce risk reduction
Workplace safety is another sensitive area that can benefit from risk management using AI. Besides machine intelligence, workplace incidents prevention also necessitates IoT adoption. Once the sensors are in place, the AI-powered risk management model tracks the state of each element. If there is a malfunction or abnormal pattern, the system sends alerts with AI proposing solutions.
Thanks to its predictive ability, the smart system can also send predictive alerts, thus preventing injuries and downtimes. Moreover, AI and video analytics can boost on-site safety procedures by monitoring and detecting safety violations. Thus, if a worker doesn’t have their PPE on, the industrial CCTV setup can automatically detect the violation.
Equipment control, temperature monitoring, acoustic surveillance, and lone workers’ safety are among other go-to incident prevention measures backed by machine intelligence. All these aim to reinforce safety and avert financial losses, and outages.
Thus, Kenco Logistics, which is a leading third-party logistics provider, has been using the sensors in their warehouses. The data collected by sensors is then analyzed via a neural network. The result of this intelligent setup is proactive injury management on site.
Security event management
Similar to fraud detection, security event management or SEM improves the security posture of a given company. This procedure detects and reports security-related events in tech infrastructure. This artificial intelligence for risk management applications often manifests as ML algorithms. The latter enables easier vulnerability detection across large data sets.
Unlike manual monitoring, AI-enabled SEM takes over the majority of threat hunting responsibilities from in-house security teams. From a technical perspective, SEM intelligence correlates events from several sources over a given period. Then, it compares the output with an organizational baseline and alerts the teams if set criteria are exceeded. As a result, no blind spots, noise-laden data or threat goes unnoticed.
Challenges of AI and risk management
Automated processing of any kind inevitably exposes innovators to added challenges and unintended alterations. Therefore, artificial intelligence and risk management introduce hidden rocks that can still be avoided with the right mindset. Below, you’ll find one of the most common limiting factors of AI-powered risk management.
Baking automation into threat detection presupposes significant expenses. The latter mainly stems from customized solutions that are necessary to prevent threat exposures at a given company.
The lack of technological infrastructure makes the bill even heftier. This problem is also aggravated by the ever-growing shortage of AI skills, which drives up the costs of adoption as well. But since the wear and tear losses outweigh the adoption costs, the latter seems justified.
Advanced data remains the main enabler of automation among high performers. According to McKinsey, almost half of all respondents either have a consistent data governance strategy or integrate internal structured data.
However, these numbers seem unattainable for over 75% of companies with no data culture. In particular, data quality often presents a challenge to getting a return on the initial investment. It means that siloed, noisy, and irrelevant data hampers successful risk management by smart algorithms.
Moreover, companies should decide on a slew of aspects when building the smart infrastructure from the ground up. In this case, legacy infrastructure is easier to modernize since the technological setup is already present. The main difficulty of design choices is often the result of lacking internal expertise. To overcome this challenge, business owners usually turn to vendors and get the necessary consulting on the right infrastructure.
According to Gartner, this buffer stops 26% of companies from embracing automation. Fair enough, some smart solutions are too complex to adopt. Thus, in 2019, Netflix awarded a 1-million prize to a developer who enhanced its recommendation system.
However, the streaming giant never adopted the innovation due to high engineering costs and complexity. Therefore, the tradeoffs between adoption needs and final value are something to think of.
How to establish an AI risk management framework
As you can see, artificial intelligence and risk management is a powerful match that still comes with challenges. To walk this tightrope, organizations have to pay due diligence to data protection, data privacy, and the costs of implementation. Below, we’ve curated the milestones of AI adoption that will help you minimize the perils of automated intelligence in your enterprise risk management plans.
Before innovating the risk management system, an enterprise should thoroughly research the potential use case. Ironically, organizations also have to analyze their risk profile regarding their reputation along with industry and legal restrictions. The pre-planning stage should also include the analysis of the value creation process and studying the cultural alignment of automation and business processes.
Based on the previous stage, the organization or vendor should list the technological requirements needed for AI adoption. In particular, data requirements are critical for the successful implementation of real-time threat analysis and response.
Thus, the type and source of data are taken note of at this stage. If the internal data is siloed or insufficient, the data engineering team establishes a data lake. The latter establishes a consistent data flow across the company and makes all data readily available for risk analysis.
Once your data is clean and formatted, engineers develop decision-making algorithms for risk analysis. Based on the chosen technology (ML, ANN, and others), it may take more or few datasets to build the model. The timeline also differs based on the complexity of your solution. Make sure that the engineering team evaluates the accuracy rate of each suitable solution and comes up with the most accurate algorithm.
Any intelligent product requires continuous evaluation and upgrades. A sensitive area like threat prevention is often influenced by ever-changing business needs as well as potential technical limitations. Therefore, regular maintenance is key to keeping the algorithm relevant and business-friendly.
Machine learning models are trained by learning a mapping between a set of input features and an output target. When the model is deployed, it is expected to generate accurate predictions whenever new data appears. In risk analysis, new data can often come from user interactions, market trends, and a whole lot of other factors. If the error rate of the model increases with fresh input, the model needs retraining.
AI and risk management: innovating with confidence
Over the last few years, the application domain of artificial intelligence has expanded. Risk management is one of the growing automation areas with great potential and business value. From fraud analysis to employee safety, automated threat detection can safeguard organizations with no manual effort.
However, this use case is still in its early stages due to the stigma and potential AI complexity. Done properly, machine intelligence can become your risk keeper and add a strategic advantage to the business.
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