Artificial intelligence in capital markets  

Artificial intelligence in post-trade processing

Post-trade processing has seen frequent changes across asset classes and the regulatory landscape. These changes, coupled with human-driven decisions across processes, introduce inefficiencies and lead to errors. Artificial intelligence can play an important role in reducing post-trade processing inefficiencies by easing decision-making through automated self-learning. AI capabilities, on effective application can significantly reduce the requirement for manual interventions, reduce reconciliation requirements, support straight-through processing, and enhance operations considerably.

Determine the right solutions with an AI application framework

A clear answer to “how will we implement AI?” can help organizations navigate the AI journey efficiently. They need to have a structured approach towards identifying and prioritizing use cases for AI implementation. The following approach can be a starting point for organizations in their journey to implement AI within post-trade processing.

Analyze business impact and technology feasibility

Business impact analysis

AI can help capital market firms generate insights to drive efficiencies, automate risk management and compliance across post-trade processes, and create value. We believe that firms can analyze the business impact of the AI use case across the following domains:

	Enhance insights and productivity: Analyze and derive insights from a large amount of data in various formats across sources to increase productivity and efficiencies (e.g., identify trends that regularly lead to trade failure).			Increase compliance to regulations: Safeguard assets to help stay compliant, saving hefty penalties traditionally paid for being noncompliant with regulations across geographies (e.g., identify potential fraudulent transactions).
	Reduce exposure to risk: Reduce exposure to risk by enabling an accelerated response across multiple capabilities like counterparty credit checks to cybersecurity (e.g., margin calls from counterparties based on news alerts).			Reduce costs and losses: Reduce losses and costs across the trading life cycle, from reconciliation to settlement and reporting (e.g., reduction in cost of capital through collateral optimization).

Technology feasibility analysis

To harness the true potential of AI, financial institutions will need to make significant changes and ready themselves for the implementation of AI solutions. Some of the factors that organizations need to consider while assessing technology feasibility include:

	Data pipeline: Access to production-grade data, ability to curate and collate structured and unstructured data, and availability of underlying technology required for data processing are important considerations.		Frameworks and infrastructure: Analyze the availability of a framework’s license or skill set to develop algorithms using multiple open-source or proprietary frameworks, along with the availability of infrastructure to support these frameworks to assess feasibility.
	Infrastructure elasticity: Availability of on-demand processing power in a cost-effective manner through scalable infrastructure needs to be analyzed to understand feasibility.		Data sufficiency: Use cases should be assessed for sufficiency of data such that AI analysis can be effectively undertaken.