This essay discusses Artificial Intelligence (AI)’s applications in the financial field, specifically in the usage on banks sector’s credit risk management. Starting with a short description of fundamental AI and financial background knowledge, followed by the shortcomings of traditional banking and further emphasized the importance of applying AI techniques in credit risk assessment along with its positive consequences. The major component forces on introducing different ML models applied in the process of credit risk management and later de-Blackbox AI technique by analyzing each ML model in detail. The toolkit example of Zen Risk developed by Deloitte helps visualized the de-Blackboxing process by providing a real-life case. The article also listed current concerns and potential risks of applying AI-generated models in the financial field at the bottom to provide a more comprehensive analysis.
In the era of data explosion, artificial intelligence (AI), as a tool that great at processing massive amounts of data in a limited timeline, has been used in various fields to save manpower and material resources. Credit risk management as a sector mainly composed of data and models in the financial field, the intervention and the integration of artificial intelligence technology has become an inevitable trend. The AI-generated model can improve efficiency and increase productivity while reducing operating costs. AI and ML are game-changer in the field of risk management due to the feature of properly addressing the risks posed by financial institutions that involved with a large number of complex types of information every day (Trust, 2020). Even though there are many prominent aspects of applying AI techniques in financial sectors, only 32 % of financial services applied in their industries, mainly on data prediction, financial product recommendations, and voice recognition. For now, the most common AI usage in the financial industry is chatbots which provide simple financial guidance to customers. A more complex application of AI in banking is to identify and eliminate fraud. However, the real potential market falls into risk management which highly related to financial institutions’ revenue (Archer Software, 2021). For better encouraging financial institutions especially the banking sector to keep completive in the market, making more profit, along with providing more comprehensive services for customers, de-Blackboxing AI-generated models becomes a priority for achieving these goals. How will the AI/ML model transform the banking system and further finning credit risk management? What is the Blackbox in this particular process? How do people interpret it? What are the benefits and negative results of this technique? Those are the questions waiting to be addressed.
- AI usage in Financial systems
1.1 Artificial Intelligence
Artificial Intelligence is the technology that allows the machine to mimic the decision-making process and the problem-solving ability of human beings by processing a massive amount of data with rapid, iterative algorithms to eventually automatically seek out its pattern. In short, like Alan Turing described, AI is “A system that acts like humans.” The most sturdy applications we encountered today like Siri, Amazon’s recommendation system is ANI (Artificial Narrow Intelligence) which is differs from the AGI (Artificial General Intelligence) because it only specifies in the particular field instead of mastering in a variety of sectors (Artificial Intelligence (AI), 2021).
1.2 Financial Systems
A financial system is a network connected by financial institutions like insurance companies, stock exchanges, and investment banks which allows organizations and individuals to perform capital transformations (Corporate Finance Institute, 2021). Along with the data explosion, processing financial dossier becomes more complex and time-consuming than ever before. The most arresting feature of the past financial system is the high dependency on human ingenuity (Part of the Future of Financial Services series from the World Economic Forum and Deloitte, 2018).
1.3 The Application of AI in Financial Service Spectrums
The introducing of AI brought a sky-level high efficiency into the financial system. Based on the CB Insights report (2018), over 100 companies indicated that their applications of AI improved communities’ performances in many aspects. Figure one maps out partial associations and their AI technique operating areas. Most AI financial service falls into nine categories: 1) Credit Scoring / Direct Lending 2) Assistants / Personal Finance 3) Quantitative & Asset Management 4) Insurance 5) Market Research / Sentiment Analysis 6) Debt collection 7) Business Finance & Expense Reporting 8) General Purpose / Predictive Analytics 9) Regulatory, Compliance, & Fraud Detection. Architectural Intelligence nearly across the entire financial service spectrum. Credit risk management categorized under Credit Scoring / Direct Lending is the ceiling priority in those many AI regulatory areas.
Figure 1. The AI in Fintech Market Map, 2017
- AI in Banking Majors
2.1 Banks Income Structure
The core of the banking business model is lending. Banks create monetary currency using the income earned from lending instruments and customer-facing activates. Other profit-making approaches including: Customer deposits, mortgages, personal loans, lines of credit, bank fees, interbank lending, and currency trading (Survivor, 2021). The majority of financial services in the banking sector are associated with lending which severely relies on the credit of the obligator. Back in 2018, 29% of customers claimed their preference of using credit cards for daily consumption (Schroer, 2021). As of 2020, 44% of U.S consumers carry mortgages and the number is growing steadily at a rate of 2% annually (Stolba, 2021). Borrowers’ behavior of repayment failure can cause banks to go bankrupt.
2.2 The Shortcoming of Traditional Lending Assessment
The audit process of issuing a loan requires a lot of manpower because customer files are often crowded with too many objective noise components. The slightest misjudgment will result in a wrong decision and further cause profit losing and injury borrower’s interest. For the individual borrower, forming a risk profile can affect one’s life to a great extent. For example, whether the individual can drive and live safely, the possibility of them being educated, and the chance of receiving medical treatment. For business borrowers, their risk picture is involved in more complex situations due to their data across a variety of parameters which need a longer period, cost more manpower and material resources to generate a holistic risk profile. Credit risk can affect a borrower’s financial status, lose the loaner’s capital, and damage both reputations (PyData, 2017). And the suboptimal underwriting, inaccurate portfolio monitoring methodologies, and inefficient collection models could aggravate these lending problems (Bajaj, n.d.).
Figure 2. overview of common steps in the lending process. ([[Graph]], n.d.)
2.3 The Importance of Applying AI in Lending Assessment
Processing a large number of credit assessments in limited timelines is the precedence for the banks to solve. The credit information’s existence in a form of dynamic data lets the AI have full leeway. Since the prominent feature of the AI technique is interpreting massive data in terse time with near-perfect accuracy (Bajaj, n.d.). AI helps the banks to streamline and optimize credit decisions in a wider range, transform noisy objective information into quantitative trading to better portrait consumers’ risk portfolios. AI-generated WAP (mobile banking application) can help banks knowing their borrowers’ financial conditions deeper but with more privacy by monitoring users’ financial behaviors. The AI-driven assessment can better analyze borrowers’ banking data, tracking their financial activities, and further avoid giving risk loans and reduce the possibility of encountering credit fraud (Use Cases of AI in the Banking Sector, 2021). AI is replacing many financial positions like data science analysts and FRM (financial risk manager) by proving safer, smarter, and more effective financial services to consumers (Schroer, 2021).
Figure 3. Machine learning surfaces insights within large, complex data sets, enabling more accurate risk (McKinney, n.d.)
- AI in Credit Risk Management
In the video above (RISKROBOT TM – Explainable AI Automation in Credit Risk Management – SPIN Analytics Copyright 2020, 2018), the broadcaster introduced RISKROBOT as a classic example of a credit risk computing AI technique and provided a cursory description of steps AI needed to portrait a consumer’s credit risk profile and generates a report. In another presentation made by PyData (2017), the reporter takes ZOPA as an example to introduce their ML technique involved credit risk management process. By comparing, it is quite obvious the fundamental procedures of applying AI techniques in credit risk management are relatively comparable.
3.1 AI Decision-Making’s Involvement in Credit Risk Management
Unlike human service banks which take days or even weeks to evaluate and process borrowing formalities, AI-driven banks provided extensive automated and nearly real-time services to the individual borrowers and SME lending. Following the local data sharing regulation, AI-assisted banks generate more accurate assessment results by evaluating clients’ both traditional data sources like bank transaction activities, FICO score, tax return histories, and new data information resources like general location data report, utility info more quickly, massively and extensively. AI’s decision-making involvement in credit risk management observes clients from sophisticated perspectives decreases the possibility of offering a risky loan by screening out potential fraud performers (Agarwal et al., 2021).
Instead of using a rule-based linear regression model, AI-driven banks built complex models to analyze both structured and unstructured data collected from user’s browsing histories and their social media to perform an objective and comprehensive analysis on individuals and SMEs who lack official credit records or authentic credit information reports. When building and refining the ML quantitative model, customers with significant loan risk characteristics are automated filtered by early algorithms. Potential default borrowers with wavery financial portraits required manual verification in the early stage and were comprehended by the ML model through categorizing more comparable cases in the self-auditing process (Agarwal et al., 2021).
Limit Assessment and Pricing
AI/ML technique allows the banks’ analysis borrowers off the record financial condition by applying optical character recognition (OCR) to extract data from non-documentation files like e-commercial expenditures from costumer’ email and their telecom records. The ML model in this intervention can dissect loan appliers’ actual financial disposition power, to provide a more rational loan amount that does not exceed the borrower’s repayment ability, and further using NLP (natural language processing) to determine the repayment interests (Agarwal et al., 2021).
ML model is also devastated in detecting the five costliest frauds: 1) identity theft 2) employee fraud 3) third-party or partner fraud 4) customer fraud, and 5) payment fraud like money laundering (Agarwal et al., 2021). A chinses bank Ping An applied facial recognition to identify the confidence level of borrowers’ financial statements. The AI-driven facial recognition mobile phone software can detect and process 54 subtle expressions in 1/15 to 1/25 of a second by tracing eye movements (Weinland, 2018).
Figure 4. The combination of AI and analytics enhances the onboarding journey for each new customer. (McKinsey & Company, 2021)
3.2 ML Models in Credit Risk Management
Support Vector Machine (SVM)
SVM is a supervised machine learning algorithm often used for individual feature classifications (Ray, 2020). By using the concept of Structural Risk Minimalization (SRM), SVM calculates and differentiates the two classes of hyper-plane in high-dimensional space and lines by using the linear model in a high-dimensional space. SVM helps to analyze credit risks by classifying the decisions to the rational breadth (Iyyengar, 2020).
Figure 5. (Kaggle: Credit Risk (Model: Support Vector Machines), 2020)
Decision Tree (DT)
Decision tree (like CRT, QUAID, QUEST, C5.0) are responsible for making predictions by inserting pre-programed decision rules subtracted from data features and generating tree-like structures terminated by decision notes which corresponding to input various. Starting from the top/rooting component, tracking down each branch that represents specific features of the borrower to find the predicted value (credit risk) (Fenjiro, 2018).
Figure 6. Decision Tree in loaning approval case, 2018
Neural Networks (NN)
Neural Networks technique is a processor which simulates the activities of the human brain to collect the detected information and store the knowledge. Three major layers in Neural Networks include the input layer, hidden layer, and output layer. Other ML models like MA (Metaheuristic Algorithm) are also fit for analyzing credit risk management. However, the application depends on hands-on situations bases on the misclassification level, the accuracy of algorithms, and computational time (Iyyengar, 2020).
Figure 7. The neural network layers for credit risk evaluation
3.3 De-Blackbox AI in Credit Risk Management
Case study of Zen Risk
The AI technique used in credit risk management is a double-edged sword that performs extremely efficiently, but the progress wasn’t transparent enough for both loaners and borrowers to further touch the bottom. Deloitte designed a de-Blackboxing tool especially for revealing the myth of the AI-driven credit risk assessment process. The platform called Zen Risk aids to help clients access, compare and study the modernist ML models for better understand, analyzing AI techniques applied in credit risk management, and also make more accurate predictions. Zen Risk as a de-Blackbox toolkit, promised its clients a complete transparency evolution, audibility process, and clear output. The Zen-Risk case study will open the Blackbox on the perspectives of the applied models, features along with general outcome explanation and individual forecast explanation (Phaure & Robin, 2020).
Starting with the advanced pre-data processing stage where data filtering, classification, cleansing, and identified outlier parameters happens. Clients with prepared data sources can determine the perfect match ML model to use (like NN, DT, SVM, MA mentioned above). Zen Risk visualized the model choosing process for users to better understanding what happens when different ML models computing the data. The solution can be integrated into a single model, or hybrid models when seeking for comprehended investigation. The straightforward solutions generally fall into simple ML models like Boosting (like LightGBM) and Neural Network. Heterogeneous classifiers, individual classifiers, and Homogeneous classifiers are the most common methods used in this stage. When encountering complex situations, applying hyperparameter optimization algorithms (like LIME, SHAP) is necessary to perform a more engaged data interaction (Phaure & Robin, 2020).
Taking the tree-like model as an example, the algorithm performed in the first stage can present the importance of each feature by assessing its quantitative value. The computing process captures the impact of manipulating a variable on the model evaluation metric. During the transformation, the decrease in model quality is often associated with the variable’s Importance and influence. For example, if value = 600 is the standard of loaning rejection, then the feature of credit amount and age indicates a highly correlated factor with whether approving a loan than the features of loaning purposes (Phaure & Robin, 2020).
Figure 8. Deloitte artificial intelligence credit risk
LIME as a local model categorized as a post-hoc model-agnostic explanation technique that explains the individual prediction of de-Blackboxing ML in credit risk assessment by the lights of other approximate easy-to-decrypted Blackbox (Misheva, 2021). Unlike tree models, LIME remains encrypted as a Blackbox that only allows the users to study what’s happening inside by providing a similar transparent model (like linear regression, decision tree). The figure below is the LIME model possessed on these succedaneums (XGBOOST Model), to explain whether if the input data (borrower) has risky potential or not (Phaure & Robin, 2020).
Figure 9. Deloitte artificial intelligence credit risk LIME model presented by XGBOOST Model
The most promising model is the Shapley value analysis (SHAP) which calculates the portion of each feature contributed in the individual prediction that is hard to accomplish by applying a simple linear function model. Unlike LIME which presents various factors of an individual, SHAP presents a unique value that indicates the direct answer onto a specific individual. The function of SHAP is showing above where f presents full feature, i presents the added features. And the next figure shows the result of a borrower is too risky for granting a loan generated by the SHAP model (Phaure & Robin, 2020).
Non-transparency and Data Bias
32 % of financial institutions displace fears of applying AI techniques and ML models in the credit risk assessment. AI-driven models are accurate in providing final outputs (making loaning decisions), but the complex calculation turns the entire thinking progress into a Blackbox which is hard to decrypt. It is more difficult for the financial institutions to explain the unqualified reasons to the borrowers other than providing a numerical result, and also hard for financial servers to report to their superiors why do these models receive these predictions (scores) (Kerr-Southin, 2021). The feature of Non-transparency in AI-generated models makes it even harder to detect and correct data bias which could deepen the discrimination.
The algorithms in the AI-generated credit risk assessment model are programmed by human beings. The programmer’s proficiency directly affects the performance level of the model. The model risk could severely harm a financial institution because is often too scaled to retrieve the loss. Any insignificant mistakes like hiring non-experience modelers and operators, no back-testing, and operational problems in the model could result in irretrievable damage. One large US bank lost $6 billion due to value-at-risk model risk. Under the regulation of protecting customer and company privacy, these failed modeling examples are growing on the tree but cannot be publicly studied. It blocks the way of learning from past experiences. Constant trials and errors have become the only effective solution at present (McKinsey & Company et al., 2015).
Through detailed analysis of several common artificial intelligence (AI) lending risk analysis models in the financial field, it is not difficult to find that because there is a large amount of complex data types got involved, while machines can conclude faster and more accurately than ever under the correct model control, but it becomes harder for humans to explain the reasons behind the scene. The AI/ML in Credit Risk Management is frankly unable to de-Blackbox to the bottom but could only analyze an individual model to help us understand more. It is worth noting that although this technology has brought many benefits to various financial organizations and individuals, such as saving manpower, material and decrease time costs, there are also many hidden concerns and potential risks like deepening discrimination cause by its non-transparency feature.
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