Regardless of any prediction, one thing is clear – AI in banking will be further applied and it will bring massive revenues. To have a better grasp on how the technology is integrated into banking, and credit risk management in particular, one should explore several key areas of adoption.
Credit decisioning
Companies with the best models have all the gains in the credit world. For example, banks often use AI algorithms to create models offering more accurate default probability and loss severity. This all comes together into better credit forecasting. This report suggests that in credit risk management, AI is applied to improve credit approval, risk determination, and portfolio management.
In addition, banks that are using AI integrate automation and near-real-time analysis of clients so as to generate credit decisions from SMEs and corporate clients effectively. McKinsey indicates that tools like AI work with structured and unstructured data that is later translated into more informed and precise credit decisions for credit risk management. Respectively, credit decisioning is among the first on the AI adoption list.
Monitoring and collections
Along with better insights into credit decisioning, institutions and organizations can apply AI to build better collection strategies. Banks receive new tools for digitizing interactive data from field visits, campaigns, and the comments of collection agents. It all reduces the burden of various nonperforming loans. AI models grant automated loan underwriting and pricing.
In terms of monitoring, banks can use AI to engage in proactive interactions with clients. The technology helps collect and analyze massive volumes of data to build a 360-degree perspective on a customer’s financial profile. As a result, such a degree of monitoring allows institutions and organizations to get the most out of loans and their collection strategies.
Looking for early warning signals
Having the ability to recognize the early warning signs of potential risk is a big benefit for credit risk management. AI works with fraud detection and ensures compliance, as these two aspects help prevent financial losses and litigation. Besides, with criminals becoming increasingly sophisticated in financial crimes, fighting money laundering and fraud is often a part of corporate social responsibility strategies.
Banks use AI in the context of investigational initiatives directed at accurately detecting any suspicious activity that can be red-flagged as a fraud. What is more, it is all done in real-time. Traditional instruments of recognizing the early warning signs of potential risk rely on many experimentally defined indications and expert judgment. Yet, such an approach includes the possibility of human error and the requirement of lengthy processing times. In turn, AI utilizes large volumes of high-velocity data and uses Natural Language Processing (NLP) to accurately generate warning signals in a matter of minutes.
Credit risk analysis
Conventional credit risk analysis often relies on complex statistical models which assume formal relationships between features in the form of mathematical equations. In turn, AI uses Machine Learning (ML) methods that can learn from data without requiring any rule-based programming. As a result of this flexibility, Machine Learning methods can better fit the patterns in data. Essentially, it is done through the following classical ML algorithms as well as deep learning techniques:
- Logistic regression – one of the most commonly used ML algorithms. It’s a supervised learning approach, which means that a user feeds the pairs of input indicators and desired outputs into the algorithm during the training, and the algorithm finds a way to produce the desired output given a specific input. Afterwards, the trained model can predict the output given new and never seen before input without any help from a human. At the heart of the Logistic Regression model is a linearity assumption, thus it can only model simple linear dependencies.
- Random Forest Method – an intuitive supervised learning method. Here the idea is to leverage the historical datasets and build them into data trees to optimize the future process of analysis.
- Support Vector Machine (SVM) – a vanilla SVM is a type of linear separator. We draw a straight line through our data down the middle to separate it into two classes. If we can’t draw a line through the data, we can transform the data so that the separation becomes possible.
- K-Nearest Neighbors (KNN) – finding the class of the data based on proximity to old data. Tell me who your closest friends are, and I will tell you who you are.
- Neural networks (NNs) – basically, these are algorithms mimicking the way the human brain operates. NNs have been shown to be effective especially for short-term predictions. They are also robust in the sense that they deal well with missing data and correlations between input variables. However, NNs are not easily interpretable, thus it is difficult to understand why the model makes the predictions it does.
These tools allow for the equitable distribution of data and create an environment for using non-linear patterns of different variables, like in credit scoring. As a result, banks receive intuitive credit risk analysis with a higher accuracy than traditional statistical methods.
It is worth noting that the list above does not entirely encapsulate the entire adoption areas of AI in banking. There are many more segments in which experts could apply the technology (see Fig. 2).
Figure 2. Adoption of AI tools by the banking area
Such a broad application area shows that AI is already bringing benefits. Further, let’s explore some key areas that are the most advantageous to adopting AI. The majority of them directly or indirectly correlate to credit risk management.
Benefits of credit AI
AI enables institutions and organizations to work with real-time transactional data. Additionally, it provides models that can operate with a wide range of various data points. Putting all these factors together, AI offers some notable advantages.
Automated and personalized decisions across a customer’s lifecycle
Financial institutions can use AI models across various use cases. Such an approach adds value by automating several dozen decisions linked to diverse customer journeys. For instance, within the initial stage of the lifecycle, banks can use AI analytics to improve aspects like customer acquisitions, deepening relationships, and smart servicing, and for these factors alone, AI makes a customer’s journey seamless and more appealing.
Better credit decisioning
The improved customer lifecycle journey leads to better credit decisioning. McKinsey suggests that AI has a proven positive impact on the credit-approval turnaround time and the percentage of applications approved. Credit decisioning driven by technology can help scale the business while lowering costs at the same time. And, this happens through the more accurate identification of riskier customers. Essentially, it means improving approval rates along with reducing credit risk.
AI-driven credit decisioning proves itself beneficial in three key areas:
- Credit qualification. AI helps analyze a large segment of consumers and accurately determines whether a particular client qualifies for a loan.
- Limit assessment. With AI-based algorithms, institutions can automate the process of determining the maximum borrowing threshold based on a number of processed factors.
- Pricing. AI-first banks are able to offer highly competitive rates and are able to adjust pricing according to market shifts.
These benefits ensure accurate credit decisioning. They lower the cost and ensure that banks obtain clients who can repay their loans and who are eligible to receive new loans.
Anticipating negligence and fraud detection
Having a solid anti-fraud system always works in the favor of any given institution or organization. It is a staple of good credit risk management. With credit relationships rapidly moving into digital channels, AI allows for the automated processing of the different aspects of loan applications, credit approvals, and funds reimbursem*nt. However, the more loans a bank can issue means a higher possibility of fraud. At this point, many institutions are integrating AI-based tools that help recognize any negligence in its initial stages.
For example, McKinsey offers the example of the company Ping An, as they use an image analytics model to recognize several dozen facial microexpressions of their clients in order to determine whether the person is being truthful in their loan application. This shows that AI can process various data points to increase the number of clients while decreasing the instances of fraud.
Optimization of the collections process
Sending a debt to collections is among the last things a lender wants to do. This is because third-party fees, from various collection agencies, absorb any margin almost instantly. Furthermore, as soon as clients start receiving a collections class, they won’t return to the lender. The reports show that it’s five times more expensive to acquire a new client than keep an existing one. This also means that optimization of the collection process can decrease customer retention and save companies a lot of money.
With AI-based algorithms, institutions use data points collected through the entire customer lifecycle. It presents a clear image of who is most likely to pay back a loan. If an institution receives a red flag, meaning that a customer is falling behind on their payments, there are things that can be done to get them back on track. For instance, lenders can offer temporary decreasing limits or new payment plans. As a result, banks gain clients who can pay them back and institutions do not need to invest in looking for new customers.
Challenges of credit AI
As with any new applications of AI, there are particular challenges that will follow. Yet, to understand how obstacles affect the domain of credit risk management, we should examine how banks prioritize their AI applications (see Fig.3).
Figure 3. Perceive AI applicationin credit risk management
IT shows early warning signals detection and fraud anticipation as having the top priority in the adoption of AI technology in credit risk management. Here are some of the challenges:
Compliance
Various AI solutions have hidden layers of decision making, which can have a profound impact on the final outcome. When using such aspects like deep learning, companies and institutions working with finance can find it hard to maintain the evidence influencing AI-based decisions. In such a case, it can adversely affect aspects like fairness and alignment with a company’s values. The report offered by Deloitte illustrates how AI-based tools increase the “risk appetite” of some financial firms, thus making them forget about their mission and vision.
Luckily, to avoid the problem, various compliance guidelines exist. Here is one guideline that explores the principles of how to use AI fairly and without harm to customers.
Governance
When dealing with AI algorithms, there is the notion of a black box design. In simple terms, it is the method that makes inputs and operations invisible to the users or any interested party. Without proper AI model governance, black box design can lead to biased decision making. The company Deloitte tries to overcome this given challenge by improving the degree of transparency involved in the functionality of its AI-based algorithms.
Going further, the challenge of governance can also correlate to the lack of business support, nonexistent links between business and quants, and ineffective internal methodological approvals. In other words, for AI to be governed properly, a company needs a well-functioning internal system of communication, and one that does not include too much bureaucracy and/or hierarchy.
Quality
AI works with data. And when there is data, there is always a challenge regarding its quality and credibility. Data obtained from non-credible sources leads to false outputs. False outputs result in bad decision-making. AI applications are designed to learn from the data that experts supply them with. Respectively, when it comes to the question of data and source quality, companies and institutions often face obstacles like problems with data gathering, the lack of appropriate external data, the lack of explainable results, and the lack of results at all.
While keeping these challenges in mind, it is crucial to understand that AI-based algorithms are not a panacea. There are particular matters with compliance, governance, and data quality that need to be addressed. Luckily, there are existing real-life examples of institutions and companies who managed to get the most of AI in credit risk management.
Examples of early AI adoptions in credit risk management
The U.S.-based FinTech startup ZestFinance showed tangible results in adopting AI for credit risk optimization. The company used the technology to reduce losses and default rates by 20%.
Bank of England applied AI in credit risk management, specifically in the area of pricing and underwriting of insurance policies. The leaders within the institution imply that AI algorithms illustrate a level of sophistication that cannot be matched by traditional models.
Another American multinational investment management corporation, BlackRock, used AI for evaluating liquidity risks. The firm’s asset managers fed the AI algorithms with internal data and were able to accurately calculate the price tag of cost liquidating. In turn, it granted higher returns and lower risk.
These are just a few examples showing AI-based algorithms working for the good of credit risk management. Without a doubt, many more success stories will follow. And, the more firms and institutions use AI, the greater the degree of control and accuracy achieved within the credit risk management process.
What’s next to follow?
The most likely path for AI to follow is for it to be coupled with edge capabilities. Essentially, edge computing uses the power of in-device computing to offer deep insights and predictive analytics in near-real time. Plus, the edge computing market is booming right now (see Fig. 4).
Figure 4. U.S. Edge Computing Market
A comprehensive research report offered by Market Research Future (MRFR) anticipates the edge computing market to reach $168.59 billion by 2030. Now, the market stands at about $14 billion. The reason for such a boost in edge capabilities stems from the tools it offers:
- Voice recognition
- NLP and voice-analytics
- Computer vision
- Facial recognition
These tools offer a foundation for AI-based algorithms. They serve as mediums for data collection and ensure that AI has enough data points to process. With an ever increasing number of people having mobile devices, companies receive an almost unlimited stream of data that can improve customer lifecycle journeys. Yet, when further coupling edge capabilities, companies and institutions should always comply with the rules of data security and the laws regarding the protection of information.
Wrapping up
It may sound like AI is the technology for the future. In reality, it has been with us for more than half a century. The first AI was introduced in 1956. However, now is the time when everyone is understanding its potential and can take the opportunity to apply the technology on a global scale. Without a doubt, the adoption of AI won’t stop at credit risk management or banking, in general, as the sky’s the limit.
AI will move further and further, and will make businesses more successful. The important question is if the companies remaining uninvolved can afford to avoid the application of AI. Getting on board with AI now grants an early start and competitive advantage, something that any company in FinTech or any other industry is constantly looking out for.
If you want to know more about trends in edge computing, click here.
If you want Avenga to help you with AI adoption in banking or another industry, feel free to contact us.
As an expert with a deep understanding of AI applications in banking, I can confidently provide insights into the concepts mentioned in the article.
Credit Decisioning:
AI Models for Credit Decisioning: The article highlights how AI algorithms are extensively used in credit decisioning to enhance credit forecasting. These algorithms analyze structured and unstructured data, providing more accurate default probability and loss severity assessments. Notably, McKinsey emphasizes that AI tools contribute to effective credit decisions for both SMEs and corporate clients.
Monitoring and Collections:
Digitizing Interactive Data: Institutions leverage AI for better collection strategies by digitizing data from field visits, campaigns, and collection agents' comments. This not only reduces the burden of nonperforming loans but also allows for near-real-time analysis of clients, leading to proactive interactions and improved 360-degree perspectives on customers' financial profiles.
Early Warning Signals:
Fraud Detection and Compliance: AI plays a crucial role in detecting early warning signs of potential risks through fraud detection and ensuring compliance. With criminals becoming more sophisticated, AI aids in real-time investigation and accurate detection of suspicious activities, contributing to corporate social responsibility strategies.
Credit Risk Analysis:
Machine Learning Algorithms: The article introduces various Machine Learning (ML) methods used in credit risk analysis. These include Logistic Regression, Random Forest Method, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Networks (NNs). These algorithms enhance credit risk analysis by learning from data without rule-based programming and accommodating non-linear patterns.
Benefits of Credit AI:
Automated and Personalized Decisions: AI enables institutions to work with real-time transactional data and provides models that operate with diverse data points. This leads to automated and personalized decisions across a customer's lifecycle, adding value to customer acquisitions, deepening relationships, and improving smart servicing.
Challenges of Credit AI:
Compliance, Governance, and Data Quality: The challenges associated with AI in credit risk management include compliance issues due to hidden layers of decision making, governance concerns related to the black box design of AI algorithms, and the crucial aspect of data quality. Addressing these challenges is essential to ensure fair, transparent, and effective AI applications in credit risk management.
Examples of AI Adoptions:
Success Stories: The article cites examples of successful AI adoptions in credit risk management by FinTech startup ZestFinance, Bank of England, and BlackRock. These success stories highlight tangible results, such as reduced losses, improved pricing and underwriting, and accurate calculation of liquidity risks.
Future Trends:
Edge Computing Integration: The article discusses the potential coupling of AI with edge capabilities in the future. Edge computing, utilizing in-device computing, is anticipated to enhance AI's capabilities, providing deep insights and predictive analytics in near-real time. The integration of voice recognition, NLP, voice-analytics, computer vision, and facial recognition through edge computing can further improve customer lifecycle journeys.
In conclusion, the adoption of AI in banking, especially in credit risk management, is a dynamic and evolving field with numerous benefits and challenges. The article effectively highlights key concepts, applications, benefits, challenges, and future trends associated with AI in the banking sector.
