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Data Analytics Driven Automation

GenAI:

The new frontier

Automating Counterparty Credit Risk

A Case Study on GenAI in Asset Management

Transforming Counterparty Credit Risk: A Case Study on GenAI in Asset Management, is a proof of concept (PoC) involving a European asset management firm with over €30 billion in assets under management (AUM). The firm needed to automate and enhance its counterparty credit assessment process. Recognised as advocates and innovators in AI/ML, and having collaborated with early adopters in financial services, Calimere Point were chosen as key partners to conduct a comprehensive PoC. Our objective was to evaluate the potential of leveraging generative AI solutions.

In this case study, we examine the implications of the generative AI (Gen AI) revolution, address existing challenges in counterparty credit assessment, demonstrate real-world applications and benefits, and validate the impact on asset and wealth management. By cutting through the hype and working with early adopters, we present facts, key limitations, and alternative approaches, helping evaluate the viability, usability, and feasibility of Gen AI features and systems in practical settings.

Navigating the GenAI Revolution: Implications for Asset and Wealth Management

What Does Gen AI Mean for Asset and Wealth Management? Generative AI is revolutionising asset and wealth management with powerful capabilities in information processing and content generation. While innovative firms are already reaping benefits, the adoption journey comes with challenges: - Legal and ethical implications of AI-generated outputs - Data security, privacy, and compliance concerns - Talent shortages and implementation costs - Technical limitations and integration challenges Despite these hurdles, our case study emphasises the critical importance of implementing GenAI now to stay competitive. It also highlights the need for responsible AI programs to ensure ethical usage and mitigate potential risks. Dive deeper into the challenges and opportunities of GenAI in asset management by downloading our full case study.
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Case Study Background

Summary

The asset management firm faced challenges in their Investment Risk process while conducting annual credit assessments for 150 trading counterparties.

Key points:

  • 100 of 150 counterparties were unlisted entities
  • Listed entities: Regulatory filings used for assessment
  • Unlisted entities: Relied solely on annual reports
  • Manual review of 100+ annual reports proved inefficient

Client Needs:

  • Optimise time and cost resources
  • Minimise data validation time and human errors
  • Explore automation for diverse document processing
  • Implement effective solutions for unstructured data

The firm required innovative solutions, potentially leveraging generative AI, to streamline operations and enhance efficiency in their Investment Risk process.

Industry: Asset Management

Key Challenges in Annual Credit Assessment

Manual Review Challenges

The manual review process is time-consuming and labor-intensive, placing a significant strain on resources. Data extraction from extensive annual reports is prone to human error. Validation procedures require rigorous checks and controls, further consuming valuable resources.

Existing Counterparty Credit Process Challenges

Report sourcing is tedious, requiring searches across various databases and websites. Data extraction involves manually opening each report and locating relevant financial information. Tracking counterparties becomes difficult due to potential name changes over time.

Risk Management Implications

Delays in accessing and analysing financial information can hinder the firm's ability to identify and mitigate potential credit risks in a timely manner, leaving it vulnerable to market fluctuations.

Annual Reports Data Complexity

Annual reports often come in inconsistent PDF formats that are not machine-readable. Key financial metrics are presented in various ways, with crucial data points sometimes labeled implicitly or using non-standard terminology. This inconsistency adds another layer of complexity to the data extraction process.

Uncover the primary obstacles in the existing counterparty credit process, including time-consuming manual reviews, data extraction complexities, and the implications for risk management. See how inconsistent data structures in annual reports further complicated the task.

Download Full Case Study

Insights and Discoveries

Potential for Automation: Summary of Critical Findings

Assessment Effort

The analysis revealed that the entire annual Counterparty Credit Assessment process consumed approximately 30 days of effort each year. This extensive time investment underscores the pressing need for efficiency improvements.

Complexity of Unstructured Data

Prime target for automation, the process faces significant hurdles due to the complexity of unstructured data sources. Manual extraction and validation processes struggle to cope with the volume and variability inherent in these sources.

Exploring Gen AI Solutions

Given the challenges posed by the complexity of the data, an intriguing question arises: Could generative AI offer a viable solution? How might it streamline data extraction and validation processes while maintaining accuracy and reliability?

Strategic Considerations

As we deep dive into the potential of Gen AI, it's essential to consider the strategic implications. How might implementing such technology impact resource allocation, risk management practices, and overall operational efficiency?
Basic Questions and Challenges

Leveraging AI for Automated Financial Data Extraction

Our case study answers the questions and challenges surrounding the use of large language models for text extraction, focusing on their ability to extract specific financial metrics from PDF reports and the availability of programmatic interfaces for seamless integration with existing systems.
Can a large language model help with text extraction?

Exploring the potential of leveraging advanced language models to streamline text extraction processes represents a fundamental query in our pursuit of efficiency.

Given a PDF, can a query such as ‘extract total assets and total equity from the attached company annual report’ find the metrics?

The ability to precisely extract critical financial metrics from PDF reports through tailored queries is a pivotal consideration in enhancing automation and efficiency.

Is a Programmatic interface available?

The availability of a programmatic interface capable of seamlessly integrating with existing systems emerges as a pivotal aspect in the quest for streamlined and automated processes.

Experimentation and Approach

Generative AI is transforming the financial services industry, particularly in asset and wealth management. For firms to harness its full potential, a dual focus is essential: understanding the technology and taking decisive action while managing associated risks judiciously. Value creation from GenAI will not only stem from advanced technology but also from cultivating a data-centric culture that invests in foundational capabilities and develops robust risk management frameworks. Successful initiatives will emerge from a blend of industry domain expertise and a culture of innovation, envisioning new ways of doing business through the convergence of GenAI with traditional data analytics and hybrid approaches.

Summary of Approaches

We explored three different approaches to automating counterparty credit assessment:

1. Pure Generative AI

Focused on leveraging advanced AI for interactive and natural language processing tasks.

2. Traditional Data Analytics

Emphasised accuracy, repeatability, and robustness through established data processing techniques.

3. Hybrid Approach with AI Assistants

Combined the strengths of generative AI and traditional data analytics, ensuring control, accuracy, interactivity, and scalability.

Overall Conclusion and Analysis

Summary

  1. Pure Generative AI:
    • Strengths: High interactivity and potential for natural language processing.
    • Weaknesses: Inconsistent results, lack of repeatability, and scalability issues.
  2. Traditional Data Analytics:
    • Strengths: High accuracy, repeatability, and robustness.
    • Weaknesses: Lack of interactivity and the need for extensive pre-processing and coding efforts.
  3. Hybrid Model with AI Assistants:
    • Strengths: Combines the benefits of both generative AI and traditional data analytics. Ensures control, accuracy, interactivity, and scalability. Facilitates complex logic integration and offers auditability.
    • Weaknesses: Requires initial setup and integration efforts to develop and deploy AI assistants.

Calimere Point brings together all the components of a solution. We built a bespoke Hybrid model with AI assistants that emerged as the most effective solution, offering the robustness of traditional data analytics with the interactive, user-friendly nature of generative AI. This method provides a scalable, accurate, and consistent way to automate counterparty credit assessments, aligning well with the firm's requirements and demonstrating a viable path forward for integrating advanced AI into asset management processes. To recap what it took:

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Ready to transform your data analytics approach?

The information presented above provides a concise overview of our findings. However, the full scope and depth of our research extend significantly beyond this summary.

Download our comprehensive case study to deep dive into these three approaches:

  • Pure Generative AI
  • Traditional Data Analytics
  • Hybrid Model with AI Assistants

Uncover valuable insights, detailed comparisons, and real-world applications that can revolutionise your data strategy.

Gain the knowledge you need to make informed decisions about integrating AI into your data analytics processes. This deep dive contains valuable information not covered in the summary – insights you won't want to miss!

Full Case Study

Opportunities with Generative AI

Given how rapidly Generative AI has taken off, the initial step is to begin with foundational education, ensuring that everyone—including the Board, C-suite, and staff—understands its importance. This is crucial for creating a level playing field across the organisation. Companies that invest time early will be better positioned to navigate GenAI most effectively.

All asset managers will need a robust GenAI strategy to maximise benefits and minimise risks. GenAI can be a transformative breakthrough for asset managers at a critical time for the industry. The natural tendency might be to adopt a "wait and see" approach, as with most technology trends. However, we believe that waiting is not an option due to the rapid growth of GenAI. Asset managers urgently need a forward-looking GenAI strategy to move forward with confidence, mitigate risks, and reap the benefits of this powerful new technology.

Calimere Point, a strong advocate of AI’s capabilities, is uniquely positioned to assist organisations in leveraging generative AI.

With our extensive background in machine learning and natural language processing, we support organisations through every stage of AI integration. Our Hybrid model is live and currently in use. Learn how Calimere Point can help make your generative AI implementation successful and explore how we can assist you in adopting AI. 

For additional insights, visit our AI/ML page to learn how Calimere Point's data science expertise and deep real-world experience in using data analytics can help your organisation define the art of the possible.

Contact Us

Peter Griffiths

Co-Founder & CEO
Peter is the co-founder and CEO of Calimere Point and has been with the firm since its inception in 2009. Prior to founding Calimere Point he spent the first 15 years of his career in Investment Banking, working within trading, structuring and risk management disciplines across a number of asset classes. Peter has a Masters in Finance from London Business School, a BSc in Economics and Finance from Oxford Brookes University and is a qualified accountant (CIMA qualification).

Dominique Nelson-Esch

Chief Marketing Officer

Dominique is a multi-disciplinary visual designer, communications and brand strategist, with a two-decade journey in collaborating with startups and SMEs. Her portfolio includes consulting for over 100 businesses globally, where she managed branding, design, and digital communications.

Dominique’s extensive background in financial services equips her with a nuanced understanding of our industry landscape, including 14 years in financial services, holding key roles such as Head of Portfolio Risk Audit and Niche Portfolio Management for major Insurers.

In her current role as Chief Marketing Officer (CMO) at Calimere Point, Dominique focuses on strategically positioning and promoting the firm. Her goal is to enhance brand awareness and establish market leadership through innovative marketing strategies that highlight Calimere Point’s expertise in delivering impactful data-driven solutions.