AI in Credit Processes: Opportunities and Risk - Be Shaping The Future

AI in Credit Processes: Opportunities and Risk

The rapid development of artificial intelligence (AI) has transformed many industries in recent years, and the financial sector is no exception. Against this background, possible scenarios for the integration of AI in the lending process of a credit institution are considered in this insight. The motivation for turning to AI is in the realisation that traditional methods of credit assessment and evaluation, such as credit score, income assessment, employment history, debt-to-income (DTI) ratio, Schufa information or manual document review, are reaching their limits, resulting in longer processing times, manual errors and sub-optimal risk assessment. These traditional data sources and analytical approaches can be time-consuming, may not provide real-time information and may not cover all relevant aspects of a borrower’s financial situation.
This insight highlights the opportunities for financial institutions to address the challenges of the traditional lending process and achieve a revolutionary transformation through the use of advanced AI technologies.

Challenges in the banking sector

Traditional credit processes are often characterised by time-consuming, manual processing of extensive data. The challenges range from lengthy credit checks to complex credit assessments that are prone to human error. These processes are not only resource-intensive for the credit institution itself and limit capacities in the back office, but also lead to longer waiting times for customers. Banks now recognise the need to rethink their approach and are looking for solutions that not only increase efficiency but also improve the quality of credit decisions.

The use of AI as an answer

In the search for innovation, more and more banks are choosing to use AI. The decision is based on the conviction that AI technologies are able to recognise patterns in large amounts of data, automate complex tasks and thus bring about a fundamental change in the credit process. This includes the use of natural language processing (NLP) to analyse documents, machine learning algorithms for a more precise credit assessment and predictive analytics to improve credit scoring.

Implementation goals

The primary goals of implementing AI in the credit process are:

  • Acceleration of the credit process: Reduction of processing times for credit applications from weeks to days or even hours.
  • Reducing errors: Minimising manual error sources through automation and more accurate data analysis.
  • Improving the customer experience: Creating a smooth, customer-friendly process that responds to customer needs.
  • Optimised risk management: More accurate predictions of credit risk to better manage the loan portfolio and reduce default risks.

Possible solutions

1. Machine learning algorithms in credit scoring: Machine learning refers to the use of algorithms and statistical models to give computers the ability to learn from data and identify patterns or trends without being explicitly programmed. The learning process takes place by exposing the algorithm to data and adjusting its internal parameters to recognise patterns and make predictions. To do this, machine learning algorithms require large amounts of data. In credit assessment, this can be historical data on credit histories, transactions, income and other relevant information to be checked. The data is collected, cleansed and structured in a format that is suitable for analysis.

Based on this data model, “feature engineering” is carried out to extract certain characteristics, known as “features”, from the data that serve as relevant indicators of creditworthiness. These include, for example, income level, existing debts, payment history, employment history and other parameters. Feature engineering is crucial in order to provide the algorithm with the necessary information for a precise assessment.

The selection of the algorithm to be used is the responsibility of the credit institution and depends on the type of data and the specific requirements. There are various machine learning algorithms that can be used in credit checks. Examples include decision trees, random forests, support vector machines and neural networks.

Before going live, the model is trained with the prepared data. During training, the algorithm learns from the existing data sets and adjusts its weightings to recognise patterns that are relevant for predicting creditworthiness. After training, the model is tested on valid data sets to ensure that it processes not only the training data but also new, unknown data well. The model is iteratively optimised to improve its accuracy and reliability.

By using machine learning in credit assessments, credit institutions can make more precise, faster and more efficient credit decisions. The algorithms are able to capture complex relationships between different factors and enable a more individualised assessment of each customer’s creditworthiness. Ultimately, the loan officer should validate the results of the machine learning model before incorporating them into the decision.

2. Natural language processing (NLP) for document analysis: In the context of document evaluation in the credit process, NLP is an advanced use of AI that aims to extract, understand and analyse information from written documents. This technology is used to optimise the credit process, particularly in the evaluation of documents submitted by customers as part of a loan application or, for example, in the provision of disbursement requirements, as well as externally created documents (e.g. loan agreements by law firms/lead managers). It would also be conceivable for an NLP model to determine which information/documents are missing from a customer within the credit decision.

NLP models are trained to search for relevant information in submitted documents. These can be, for example, payslips, bank statements, employment contracts or other financial documents. The NLP system automatically extracts key information such as income, expenses, length of employment and other audit-relevant data. The model enables the identification of specific entities in a text, such as names, amounts or dates. These entities are categorised to understand and organise the different types of information. NLP models also provide the ability to perform keyword analysis, context understanding and syntax analysis for grammar and structure to ensure that the extracted information is interpreted correctly. Furthermore, these models can be adaptive, adapting to new document structures or terminology in order to deal variably with changing documents.

Another possible use case in the credit process would be the reading of system-relevant data from externally created contracts, which are often written by authorised law firms. Read our case study on this from our Commercial Banking division (Digitalization of the Wholesale Credit Process utilizing AI).

The use of NLP in the credit process enables credit institutions to extract information more efficiently and precisely from a large number of documents. This leads to faster processing of loan applications, a reduction in manual labour and contributes to improved decision-making in the credit sector.

3. Predictive analytics for credit assessment: Credit institutions can also use predictive analytics to take risk assessment to a new level. Advanced statistical models and machine learning are used to predict future credit risks and actively manage credit portfolios. The algorithms analyse historical data and current market trends to create a more accurate forecast of each customer’s creditworthiness. This makes it possible to customise the credit conditions according to the specific risk profile of each applicant. Unlike machine learning, which focuses on the development of algorithms and predictive models that learn from data and automatically recognise patterns, predictive analytics is concerned with analysing historical data to develop models that predict future events.

As with machine learning, extensive data aggregation (credit histories, transaction data, loan application information, payment histories, existing debts, credit history, length of employment, income and other relevant parameters) forms the basis of the models to be developed. Here too, the predictive analytics model is trained, validated and optimised until it can be integrated into the operational credit process. It automatically assesses an applicant’s creditworthiness and supports the decision-making process by predicting the probability of loan defaults or delays. By continuously monitoring and adapting the model, financial institutions can take timely action to optimise their risk position.

The application examples listed illustrate how the intelligent use of AI can revolutionise the credit process in financial institutions. The synergy of machine learning, natural language processing and predictive analytics not only enables a rapid increase in efficiency, but also an optimised risk assessment. The success of such implementations demonstrates the immense potential of AI in the financial sector and shows that innovative technologies can pave the way to a more agile and customer-centred financial world.

Data protection requirements for the introduction of AI

Nevertheless, the introduction of AI can also present hurdles. Compliance with data protection regulations is crucial in order to maintain customer trust and fulfil regulatory requirements. AI systems require large amounts of data in order to function effectively. When collecting and storing this data, banks must ensure that they observe the principles of data minimisation and purpose limitation. It is important to clarify which data is actually necessary for the AI applications and ensure that the data is stored in accordance with data protection regulations. Transparency and explainability to the customer is particularly important with AI models, especially with complex neural networks, which are often known as “black boxes” because their decision-making is difficult to understand. Customers must be able to understand how their data is used and how AI models arrive at certain decisions. Credit institutions must also ensure that data is only used for the specific purposes for which consent was given and that it is not used for other purposes without authorisation, and that customers have a right to know how their data is being used and, if necessary, to have it deleted. Credit institutions must implement mechanisms in their AI models to ensure these rights, especially if AI models have been trained on historical data that is no longer relevant or necessary.

Financial institutions must implement strict security measures to ensure the protection of data from unauthorised access or data leaks.
In addition, financial institutions serving international customers should be aware of different data protection regulations and laws in different countries. This requires careful coordination to ensure that the AI applications are compliant with the respective laws.

Regulatory requirements for the use of AI

The legal framework under consideration includes, in particular, the world’s first regulation of AI, which, in the form of the European Union’s AI law, is now imminent following its approval by the EU Parliament in March 2024. The aim of this regulation is to make the use of AI safer. AI systems should therefore be more transparent, comprehensible, non-discriminatory and environmentally friendly. The core of the regulation is the classification of AI systems in four levels according to their risk potential and the corresponding definition of obligations for providers and users. Another important aspect is the requirement that AI systems are monitored by humans and not purely automatically in order to prevent harmful effects. A separate Insight will follow on these and other developments in AI regulation.

In summary, when introducing AI technologies, it is important that credit institutions develop a comprehensive strategy for compliance with data protection and regulatory requirements and adopt implementation practices that ensure the privacy of their customers. This must include close co-operation with data protection officers, clear guidelines and training for employees, and the use of privacy by design and privacy by default.

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