Type: GICP Expert View

AI in Credit: 5 Tips for Corporate Credit Analysts

In the fast-evolving world of credit, staying ahead of industry trends and leveraging cutting-edge technologies is more than a competitive advantage—it’s a necessity. One of the most transformative forces in the industry today is artificial intelligence (AI), with its ability to process vast amounts of data, uncover hidden patterns, and make predictions with unprecedented accuracy.

The utilization of AI goes beyond the mere automation of tasks. It facilitates deeper insights into clients’ financials, industry trends, and market movements. By providing a more comprehensive understanding of a company’s creditworthiness, AI enables analysts to make more informed decisions, manage risks more effectively, and even predict potential defaults before they occur.

Furthermore, AI-powered tools can process and analyze both structured and unstructured data from diverse sources, offering a more holistic view of a company’s financial situation. This ability to leverage alternative data sources opens up new avenues for credit analysis, enabling analysts to consider factors that were previously difficult to quantify or analyze.

AI’s real-time monitoring and early warning systems can help analysts stay alert to changes in a company’s creditworthiness, allowing for proactive risk management. AI’s ability to identify industry-specific trends and risks adds another layer of depth to the analysis.

However, to fully leverage the benefits of AI, it’s essential for corporate credit analysts to understand how to effectively integrate these technologies into their workflows. Here are five tips for credit analysts looking to make the most of AI in their analysis:

  • Embrace AI-powered tools: Explore and integrate AI-driven platforms that can help you streamline processes, access diverse data sources, and enhance risk assessments. These powerful tools can significantly improve your workflow. For example, AI tools can automate the collection and processing of financial data, allowing analysts to focus on high-level analysis instead of tedious data gathering.
  • Leverage alternative data sources: AI can process vast amounts of structured and unstructured data, including unconventional sources like social media, news articles, and online reviews. Tap into these alternative data sources to gain a more comprehensive understanding of your clients’ creditworthiness. For instance, AI can analyze social media sentiment to provide insights into a corporation’s brand strength and customer satisfaction, which can indirectly impact creditworthiness.
  • Stay ahead with real-time monitoring: Utilize AI-based early warning systems and real-time monitoring to proactively identify potential issues and changes in creditworthiness. By staying alert to evolving risks and opportunities, you can mitigate credit risk and make better-informed decisions. As an application, AI can continuously analyze real-time financial data to provide instant alerts on potential credit risks, enabling timely response.
  • Deepen sector-specific insights with AI: Harness the power of AI to discern trends, risks, and opportunities unique to specific sectors. By interpreting vast amounts of industry data, it uncovers nuanced patterns, thereby enabling the generation of precise, insightful credit recommendations. For instance, AI can sift through extensive supply chain data and flag potential disruptions that could impact a manufacturing company’s credit profile. This level of detail is often not feasible with traditional analysis methods, enhancing the precision and depth of credit assessments.
  • Continuously learn and adapt: The world of AI is constantly evolving, so it is important to stay updated on the latest developments and best practices. Attend webinars, workshops, and conferences, and network with industry peers to exchange knowledge and experiences to stay up-to-date with the latest AI tools and techniques for credit analysis.

About the Author

Aneta Buchert was previously a Senior Director at the Global Institute of Credit Professionals. She has over 15 years of capital markets experience, including 10 years in Debt Capital Markets origination at Merrill Lynch and Lloyds Banking Group. She also worked at the Bank of England, where she was a contributor to the AI Public Private Forum, and a participant in the AI in Financial Services working group at the World Economic Forum. Aneta holds an MSc in Management and Economics from ESCP Europe and City University London.

Evolving Data Systems in Credit

Excerpt from the Global Credit Certificate, Chapter 19: Trends in New Technologies and their Impact on Credit

Over the last decade, the broader financial industry has seen rapid adoption of new technologies ranging from banking apps over cryptocurrencies and machine learning to electronic authentication and customer scoring. Technology has helped make markets more efficient in a multitude of ways, from achieving cost reductions to the creation of completely new assets. In this way technology in the financial industry has influenced the evolution of economies more broadly. A closer look at its impact reveals that the identification, generation and processing of data is at the core of most recent technological developments, especially in the credit industry.

Consumer finance is the most prominent financial sphere where technology changed the way transactions are made and data is used, with banking apps, cryptocurrencies, automated recommendations and telematics-boxes in cars permeating the lay-user’s day-to-day financial life. However, equally transformational is the use of technology at investment management and credit institutions where automation of data sourcing, tools for portfolio management, transaction support and automated trading have become or are becoming commonplace.

Technological Adoption

Institutions with large credit risk exposures, most prominently banks, have rapidly increased the technological sophistication of their risk management in areas such as measurement, reporting and stress testing. The volume and quality of data sources have experienced a corresponding growth, from traditional channels but also from textual data and alternative data sources. With the expanded capture and utilization of data, issues pertaining to the regulation of personal data has arisen and evolved with it.

Financial products inherently involve trust between two or more parties and carry uncertainty about future payment flows, with financial transactions relying heavily on good quality data to support their origination, monitoring and servicing. Modern banks and other financial institutions collect data from a wide array of sources to combine them for pricing, marketing, application processing, decisioning, reporting, risk management and many other operations.

Data Ingestion and Processing

For most financial institutions, the sourcing of data is a fragmented operation. Frequently used and readily available data from external providers are typically ingested via web-applications or application processing interfaces (APIs). Most institutions also collect specific data from customers or trading partners through forms, invoices and legal documentation, with financial institutions worldwide at varying degrees of transforming legacy systems that still require slow processing of frequent but unautomated information. While most organizations aim to maintain a well-catalogued ‘data lake’ for all data services, technical and procedural necessities lead to some data processing being kept separate from the data lake.

Data Procurement

Financial institutions have long been relying on traditional data such as customer information and macro-economic time-series. Automation to gain efficiencies has led to more and more specialized functions or outright outsourcing of the gathering process. Consumer information, for example, is often no longer collected in a bank branch, but rather at shops (in case of consumer loans) or estate agents (in case of mortgage loans).

In institutional lending and investing, specialized companies focus on the collection of financial data from the companies’ websites and the convenient provision of financials via ‘spreading tools’. Similarly, specialized data companies, such as CEIC Data or Haver Analytics, pool data from national statistical agencies to make them available in convenient user interfaces or directly via APIs. Market data like bond yields, indices, option prices and equity prices are often sourced from large specialized data redistributors, like Bloomberg, Refinitiv, FactSet or ICE who in turn maintain relationships with the exchanges that generate this information.

More recently, textual data is also sourced and redistributed through the APIs of specialized providers that process raw company filings and other reports. For credit risk assessment, external scores and ratings are provided by specialized institutions like credit reference or rating agencies.

Alternative Data Sources

Data collection has been made easier and cheaper by increased electronification of transactions and communication and the fall in prices for sensors. Such data can be more timely, provide a new perspective or serve as support for traditional data. Credit analysts may therefore be faced with new types of data which must first be evaluated for their meaningfulness and reliability; maybe more so than they were used to with well-established data sources.

Consumer behavior, for example, is now tracked every time the individual is paying by card, mobile phone or online, walking across cell-phone locations, chatting via apps or posting on social media. Analysis of properties and locally active businesses can benefit from granular price information, which is often fed by land registries or surveyor valuations conducted for the purpose of mortgage loans. Satellite images have also proven useful to identify trends in customer footfall and to gauge demand for toll roads, bridges, ports and car parks. During periods of economic stress, such as the global pandemic in 2020/21, such data have been consulted for a more timely update of economic activity and business disruptions and their impact on business disruption or recovery.

Technologies Accelerating Data Integration

Advancements in data technologies are accelerating the pace of integration of the varied data sources above. Textual processing and machine learning methods are transforming the ingestion and analysis of textual information to accelerate news-flow consumption across an increasingly crowded information space for credit analysts, allowing a for more efficient consumption and prioritization of more critical news and data releases to better inform credit analysis. The use of Python coding by a new generation of credit analysts allows for more direct access to primary data series in data lakes to enrich research and analysis.

Equipping Credit Professionals for Tomorrow

What next for the global credit markets?

The Global Financial Crisis was a watershed moment in the history of the credit markets, requiring as it did a massive amount of central bank support to avoid a complete collapse of the financial ecosystem. As the crisis receded, a new era of rapid expansion and change in the global credit markets began.

Over the last decade, the credit markets have embraced new technologies and new credit themes have emerged. The economic impact of geopolitical tensions and the rise of sustainable finance have become the two dominant credit themes of today. Meanwhile, the threat to businesses and governments posed by cybercrime continues to grow.

The Covid-19 pandemic has had a massive impact on the global economy and supply chains in many sectors remain disrupted. Credit professionals will be grappling with the impact of rapidly rising inflation in the near term. Making sense of how major global developments such as these will impact credit portfolios will continue to be a challenge.

Global credit megatrends

As credit professionals we spend a lot of time analyzing credit data and thinking about future trends in credit markets. As we look ahead today, we have identified six credit megatrends that credit professionals will need to think carefully about:

  • The impact of what may be an uneven economic recovery from the pandemic
  • Higher levels of sovereign and corporate debt will change credit risk profiles
  • The digital transformation will gather pace
  • There will be a repositioning of investment portfolios
  • Sustainability will become a central issue
  • There will be opportunities in Asia’s credit markets

New ways of working

It goes without saying that credit professionals should have a solid grasp of credit fundamentals and a robust analytical framework in which to operate in order to critically assess the impact of events on the credit quality of portfolios and borrowers. The impact of the six megatrends will also require credit professionals to become more flexible. Those who are willing to adapt and develop their skill sets are likely to enjoy greater career success in the credit markets of the future. Perhaps the most fundamental area of change will be how the ongoing integration of ESG considerations into the credit industry’s priorities and frameworks reshapes how the industry assesses credit risk. With so much regulatory focus on this area, and with the technological change that underpins much sustainable development happening so quickly, credit functions at financial institutions will be a fast-evolving environment. The good news for credit professionals is that new areas of specialization will open up, bringing new career opportunities.

Managing data effectively will become a bigger challenge as well. Credit professionals are being inundated with ever greater volumes and granularity of client data and market information. With so many stakeholders competing to provide information, a key challenge will be to distil this down into what’s important, actionable and ultimately leads to better credit decisions being made.

We’ve all seen how the adoption of emerging technologies has been changing the workplace environment and how, and where, work actually gets done. The pandemic has forced companies to adapt and has accelerated fundamental changes in working-patterns. Nobody can be sure what the future workplace environment will look like, but it seems likely that credit professionals will need to be ready to embrace change.

The Global Credit Certificate qualification

With change comes opportunity. Now, more than ever, credit professionals need to keep their credit analysis skills up-to-date. To satisfy the financial industry’s need for a relevant and easily accessible learning solution, the Global Institute of Credit Professionals has launched the new Global Credit Certificate. The principal aim of the Global Credit Certificate is to help credit professionals to make better credit decisions. The program is designed to assist those who want to upgrade their core credit skills so that they can excel in today’s challenging credit environment and advance their careers. The syllabus is wide-ranging, making this a study program suitable for those working in credit and related roles in most sectors of the financial industry. It consists of two levels, and encompasses two exams. It is a timely tool to help professionals prepare for changing credit conditions.

About the author

Eugene Chiam has been a credit ratings analyst at Fitch Ratings in London since 2010. He joined the Sovereigns Group in 2012, covering sovereign ratings across Western Europe, Emerging Europe and Sub-Saharan Africa sovereign credits. Prior to joining the Sovereign Group, Eugene was an analyst for the Financial Institutions Group, where he published research analysis on banking sectors. He was also lead ratings analyst on several Supranational financial institutions in 2013-2015. Key highlights in his career included analyzing the creditworthiness of Azerbaijan and Armenia through the 2020 war, responding to bank failures in Andorra and San Marino, and removing the Negative Outlook on the United Kingdom’s rating since “Brexit”. In 2021, Eugene was awarded Fitch Ratings’ Analytical Contribution & Excellence (ACE) Award for his market-beating early recognition of the pandemic’s impact on Cabo Verde’s economy and credit ratings.

ESG and Credit: What We Need to Know

The Covid-19 pandemic has not only triggered a robust debate into ESG, but also facilitated a key shift in the way both our societies and global financial services firms operate. We live in a world of scarce resources and economic growth in itself is not enough. Now, we are tasked with rebuilding our economies and enabling our businesses to place a greater emphasis on corporate purpose and governance as part of our credit decision-making and investment choices.

As the financial services industry gears up to respond to The Bank of England’s ‘green mandate’ and changing customer demands, ESG has become a key topic of conversation in the Boardroom. So, companies need to take a much more proactive approach to embed ESG metrics into their strategies right from the start in order to survive and thrive under increasing corporate and regulatory scrutiny. Firms can accomplish this task by focusing on three pillars of sustainability – people, planet and profits – which indicate that economic growth is only possible if we have environmental protection and social progress in their evaluations. This brings us onto the topic of triple bottom line accounting, a phrase coined in the late 1990’s. Companies must focus as much on social and environmental concerns as they do on profits.

Given recent developments, it’s most important for analysts to understand how ESG risks apply to credit analysis and the challenges with integrating ESG issues as part of any credit assessment. In addition, it’s crucial for credit risk specialists to understand how they can drive credit losses, climate risks and the specific regulations for banks. Assessing the materiality of both ESG risks and drivers is also key.

Let’s start by providing some clarity around what we mean when we talk about ESG risks. Firstly, the E stands for environmental risk and includes things such as climate change, rising sea levels and water pollution. Then there’s the S for social, which includes resource management, work rights, equality and diversity factors. Finally, it’s the G for governance, which looks at Board independence, shareholder rights and corruption, amongst many other issues.

ESG is not new, it’s been a theme since the early 2000’s and has been used mainly in investment management up until recently, but until now we haven’t specifically teased out these categories of risks and placed them under the credit spotlight. Credit analysis has always included ESG as part of the assessment of the credit worthiness of an obligor. For example, examining the legal, reputational and financial consequences of certain banking activities and practices. However, industry studies have shown that ESG integration techniques can help to identify unknown or undervalued credit drivers and that material ESG issues can be key drivers of credit quality.

If we turn to environmental risks, the most obvious risks have always been recognized with a tendency for focus on climate, but there are non-climate related risks that we need to consider too. Social risks are less developed in analysis and difficult to define, but perhaps most controversial and more prevalent than initially assumed as part of a thorough analysis. Governance is a topic which has been traditionally incorporated within our risk assessments and is perceived to be the factor with the largest impact on credit risk in general. The key difference when assessing ESG credit risk vs. traditional credit risk, however, is the timeline; ESG factors can take much longer to develop.

The European Banking Authority (EBA) defines ESG factors as Environment, Social or Governance matters that may have a positive or negative impact on the financial performance or solvency of an entity, sovereign or individual. In the credit world we are focusing on the impacts (either positive or negative) on counterparties or invested assets.

If we examine the bigger picture from a regulatory standpoint, the central banks and regulators are taking an active role in assessing ESG risks via the banking system, so it’s really important that we understand how to analyse these risks. The EBA has provided a useful model in its report on the Management of ESG risks for Credit Institutions and Investment Firms, which helps risk managers to split their ESG and credit risk analysis into three areas: the risk drivers, the transmission channels though which they may become key financial risks, and five well-established financial risk categories themselves (which the banks have been reporting on for years), i.e. credit risk, market risk, operational risk, liquidity and funding risk and reputational risk. The EBA also considers that ESG impacts are not just institution specific but may affect the financial system and the economy as a whole with potentially systemic consequences.

So, having identified the risk drivers, let’s take a closer look at these transmission channels. These are the causal links or chains that explain how the ESG factors we identify translate into financial risks for institutions, either via assets or counterparties, clients or suppliers. So, basically, these are the ways that any firm may feel the ‘ESG pain’ and these are all key metrics that we would need to look at in our credit analysis. Transmission channels may include: lower profitability, lower real estate value, lower household wealth, lower asset performance, increased cost of compliance and increased legal costs. It’s important to take a long-term view of each of these channels as they are often very slow to develop and we should also define their materiality i.e. are they of sufficient magnitude to affect the credit profile? Do we need to act now, or can risk mitigants be put in place?

It is not easy for firms to assess their ESG-related financial risks. Whilst accounting standards mean that the ability of analysts to understand the financial dynamics of a business are well known, the same cannot be said for the ESG dynamics of a borrower. The lack of standardized metrics across borrowers means that most analysis tends to be piecemeal and comparing ESG across businesses is a challenge. There are some specific challenges that we face as analysts, which include: uncertainty, data scarcity, methodological constraints with historical data, a longer time-horizon, non-linear effects (the risk development may not be logical) and multi-point impacts. For the time being, credit risk managers and analysts may have to handle a lot of data, but deal with a lack of usable information.

Risk reporting is an opportunity for an organization to show that it is on top of its risks, that the management and the Board understand what the risks are and why they’re taking them, that they have considered what they need to do about them and that they know that they are being managed according to plan. To date, credit analysis has been short-to-medium term only and we need to widen that time horizon, and crucially, we also need more forward looking ESG analyses and metrics. The management and quantification of climate-related risks is currently more advanced, whilst social and governance risks are mostly managed in a qualitative manner, but things are starting to change, mostly driven by regulations. The Sustainable Finance Disclosure Regulation (SFDR) coming into force in 2022 should help to improve the situation.

Basel regulation requires capital to be assigned to most of these risk categories, either by the standardized approach or internal model-based methods. So, if ESG risks are material enough, then they’re ultimately going to feed into the capital calculations of affected banks. That is why ESG should not be treated as a separate section in any credit analysis; it is like a mosaic – interwoven through the whole analytical piece with connections that reach into every aspect of analysis.

ESG and credit are closely intertwined; ESG risks can and do impact the probability of default, exposure at default and loss given default. Furthermore, an institution’s failure to accurately address ESG issues can lead to reputational damage, misconduct risk, pricing errors, lower investor confidence, liquidity issues and higher funding costs. Whereas, on the positive side, the successful management of ESG risks should in theory lead to a more sustainable business with an improved credit profile, lower cost of capital and lesser risk of loss.

About the Author

Jo Lock brings two decades of credit industry experience, specializing in credit risk management in financial institutions. Prior to joining Fitch Learning, Jo worked in risk management roles in the financial services sector covering portfolios across Europe, Africa, the Middle East and Asia Pacific regions at companies including BNP Paribas, Canadian Imperial Bank of Commerce and Northern Trust. Jo holds a degree in Commerce with German from Birmingham University.