Data & Feeds team
One of the biggest trends for fixed income in 2024 is the application of artificial intelligence (AI) to a number of fixed income use cases. However, while AI for fixed income promises to deliver significant value, firms need to deploy data governance to ensure AI is delivering the right outcomes.
- The number of use cases for AI in fixed income is growing, and includes algorithmic trading, portfolio optimisation and risk management.
- To drive the most value from AI, firms need to ensure that their data is high quality by employing data governance
- Working with a single vendor that offers the breadth of fixed income data can help make robust data governance for AI projects easier to achieve
Even though AI applications are emerging across the whole of the financial services industry, the development of use cases for AI in fixed income has happened at a slower pace in the past. In part, this is because of the lower liquidity levels in many fixed income markets, and because of the doldrums the asset class sat in prior to the sharp increase in inflation, and then interest rates, since January 2022.
However, the application of AI to fixed income use cases is beginning to accelerate as a result of increased trading activity in the asset class. Key use cases for AI in fixed income include:
- AI-driven algorithmic trading – Although not all types of fixed income markets lend themselves to algorithmic trading, the more liquid ones do. AI-driven algorithmic trading is on the increase as asset managers seek innovative ways to generate alpha.
- Automated credit scoring – For decades asset managers relied on credit ratings to assess the creditworthiness of entities issuing debt. However, although today more asset managers are bringing credit scoring – used to predict the risk of default – in house, many are using manual processes to compile a score. Using AI can enable investors to combine a wide variety of data sources with a more automated process, boosting both the quality of the score and the speed to insight.
- Portfolio optimisation – Amassing material about a company through manual research can take time and cost a significant amount of money. AI can deliver the insight faster and combine additional sources such as news and social media sentiment with financial data so that asset managers can adjust their portfolios ahead of their peers.
- Best execution – AI could be used to assess the best possible execution for a specific trade. AI models can combine historical data trends with current market conditions to help determine what best execution looks like with more precision than a human can achieve manually.
- Risk management – AI’s pattern-seeking skills and other capabilities make it a great tool for helping firms manage risk through enhanced analytics. While risk management decisions must be made by humans, AI can deliver insights that can enable firms to manage risk with agility.
Applying AI to fixed income can be transformative for an asset manager by increasing efficiency, boosting agility, and creating opportunity for alpha enhancement. However, to truly benefit from AI, firms need to ensure they have the right data infrastructure in place.
Data governance and AI
For all AI use cases in fixed income, working with high quality data that sits within a robust data governance infrastructure is essential. Using inferior quality or poorly managed data can result in significant errors, and so the positive impact that data governance has on AI use cases makes it important. When implementing a fixed income AI use case, it’s important to ask the following types of questions:
- Who owns the data?
- How is the data used in other use cases internally?
- What permissions are required to use the data in AI?
- Where is the data stored?
- How well is the data maintained?
One way to improve data governance for fixed income data is to work with data in the cloud. With cloud-based data – managed either internally or by a vendor – there can be a single source of the data across the entire organisation, front, middle and back office. This can simplify data management.
Another way to boost data governance is to work with a single vendor who can be trusted to deliver high data quality across a broad array of data types – including real-time data, fixed income pricing data, index data, reference data, historical data, regulatory data, and data derived from analytics. By working with a single vendor, data can be more easily aligned – for example, by using symbology – reducing data errors and other forms of operational risk in AI-based projects.
In short, working with AI for fixed income has tremendous potential to drive value creation for asset management firms. However, it’s vital that firms get data governance right, to ensure that AI use cases for fixed income deliver trusted outcomes. Partnering with the right data vendor is a keyway to enhance data governance and support the development of use cases for AI in fixed income.
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