AI-Driven Reconciliation: Addressing the challenges in the financial services Industry

The accurate and timely reconciliation of financial data is not merely a necessity but a cornerstone of operational efficiency and regulatory compliance. However, traditional reconciliation processes are dealing with increasing data volumes, diverse data formats, and the inherent complexities of modern financial instruments. 

This article explores the significant challenges encountered in current reconciliation practices and delves into how Artificial Intelligence (AI) driven reconciliation solutions offer a powerful pathway to alleviate these challenges, enhance automation, and ultimately empower reconciliation teams to focus on higher-value activities.

AI-Driven Reconciliation: Addressing the challenges in the financial services Industry

Manual Matching Reconciliation

In our recent study of 55 datasets from financial services and consumer payments platform organisations, we saw an average of a seemingly respectable Straight-Through Processing (STP) rate of 94%, with teams still contending with a substantial volume of manual matching - from 400,000 to over 1.1 million transactions per month.

This translates to a significant daily workload for their teams, in general ledger, digital payments, equity, corporate, and derivatives. This reliance on manual intervention, even with well-established reconciliation systems, underscores a critical industry-wide challenge: the limitations of rules-based systems in handling the nuances of real-world financial data.

Our study highlighted several key challenges associated with this manual burden:

• Time-Consuming and Resource-Intensive: Manually matching hundreds of thousands of transactions monthly consumes a significant portion of the reconciliation team's time and resources. This not only impacts their capacity for other critical tasks but also contributes to potential delays in reconciliation completion.

• Operational Bottlenecks: The sheer volume of manual work can create bottlenecks in the reconciliation process, hindering the timely identification and resolution of discrepancies. This can have knock-on effects on reporting and overall financial visibility.

• Increased Risk of Human Error: Manual matching is inherently susceptible to human error, particularly when dealing with large datasets and tight deadlines. These errors can lead to inaccuracies in financial records and potential regulatory issues for financial institutions.

• Limited Scalability: Current manual processes may struggle to scale effectively as each organisation expands their global reach and onboards new clients with bespoke requirements. The increasing demand for tailored solutions further exacerbates this challenge.

• Stifled Innovation and Proactive Analysis: With a significant portion of their time dedicated to manual tasks, experienced reconciliation analysts have less opportunity to identify patterns, propose automation improvements, and contribute to more strategic initiatives within the organisation. After years of performing the same manual tasks, the ‘curiosity or the willingness to look for automations’ can diminish within back-office operations teams.

  
  

The Architectural Drag of Legacy Systems

The study also shed light on the limitations of existing on-premise reconciliation systems. While robust and functional, the sometimes outdated architecture presents several drawbacks:

• Lack of Modern Features: The absence of cloud-based capabilities, integrated dashboards, and crucially, AI or Machine Learning functionalities, puts them at a disadvantage compared to more modern solutions. This lack of agility hinders their ability to adapt to the evolving demands of the market and their clients.

• Reliance on External Tools: The need to outsource dashboarding to business intelligence platforms adds complexity and potentially delays access to critical insights for reconciliation departments. An integrated solution would provide a more streamlined and efficient overview of reconciliation performance.

• Difficulty in Adapting to Bespoke Client Needs: Legacy system, built up with layers of customisations to accommodate various partners, struggles to efficiently handle the increasingly bespoke requirements of new clients. This lack of flexibility can slow down onboarding processes and potentially impact client satisfaction.

The Promise of AI-Driven Reconciliation

AI-driven reconciliation offers a transformative approach to address the challenges highlighted in our study. By leveraging the power of machine learning, natural language processing (NLP), and other AI techniques, these solutions can significantly enhance automation, improve accuracy, and empower reconciliation teams.

Here’s how AI can revolutionise the reconciliation process:

• Intelligent Matching Beyond Rules: AI algorithms can learn complex patterns and relationships within historical reconciliation data, enabling them to identify matches that traditional rules-based systems would miss. This is particularly valuable in scenarios with incomplete or inconsistent data, such as the "one-to-many" relationships encountered in derivatives and other reconciliation.

• Automated Identification of Anomalies: AI can be trained to detect unusual patterns and anomalies in reconciliation data, flagging potential discrepancies for human review. This proactive approach can significantly reduce the risk of errors and fraud.

• Enhanced Data Extraction and Transformation: NLP can be used to intelligently extract and interpret data from various document formats, such as SWIFT messages (MT940s, 950s, 535s), reducing the need for manual data entry and transformation.

• Self-Learning and Adaptive Systems: AI models can continuously learn from new data and feedback, improving their matching accuracy and efficiency over time. This self-learning capability reduces the need for constant manual rule updates and maintenance, a task currently handled by the IT experts within reconciliation teams.

• Improved Workflow and Collaboration: AI-powered platforms can streamline reconciliation workflows, automate the allocation of tasks to the appropriate teams, and provide enhanced visibility into the reconciliation process. 

• Predictive Capabilities: Advanced AI techniques can potentially predict future reconciliation issues based on historical trends, allowing teams to proactively address potential problems before they escalate.

Addressing Reconciliation Specific Challenges with AI

Based on our studies insights, an AI-driven reconciliation solution could offer significant benefits for reconciliation teams within financial services and consumer payments firms:

• Reducing the Manual Matching Burden: By intelligently matching a significant portion of the average 6% of manually processed transactions, AI could free up valuable time for reconciliation team to focus on more complex investigations and strategic initiatives. The potential for an immediate 60-80% reduction in manual matching, could translate to a substantial saving in FTE costs and improved team capacity within reconciliation departments.

• Improving Efficiency in Derivatives Reconciliation: The "one-to-many" relationships in equity swaps, collateral, and OTC accounts pose a significant manual matching challenge for derivatives reconciliation teams. AI’s ability to learn complex relationships could significantly improve automation rates in these intricate reconciliation flows within the investment management industry.

• Enhancing Scalability and Adaptability: A cloud-based AI-driven solution with open APIs would provide the agility and scalability required to handle increasing transaction volumes and adapt to the bespoke needs of new clients across the financial services sector. 

• Elevate the Existing Solution with AI: The "AI add-on" approach could offer a less disruptive and more cost-effective way to enhance existing infrastructures within financial service firms.

• Automating Model Governance: AI-powered platforms include automated monitoring and retraining capabilities, ensuring the ongoing accuracy and effectiveness of the matching logic. This would reduce the reliance on manual rule tweaking by in-house teams and provide a more robust and transparent governance framework for reconciliation processes.

• Empowering the Reconciliation Team: By automating mundane manual tasks, AI can empower the experienced team to leverage their expertise in more strategic areas, such as identifying root causes of discrepancies and improving overall reconciliation processes within the financial organisation.

The Future of Reconciliation

The studies insights into the challenges faced by reconciliation teams underscore the limitations of traditional, rules-based systems in the face of increasing complexity and data volumes within the financial industry. 

AI-driven reconciliation offers a compelling solution to these challenges, promising significant improvements in automation, accuracy, and operational efficiency for financial institutions. 

By embracing these innovative technologies, organisations can empower their reconciliation teams to move beyond manual processes and focus on higher-value activities that contribute directly to the organisation's success. 

Exploring AI-powered add-on solutions represents a strategic move towards a more agile, scalable, and efficient future for reconciliation operations within the financial services and consumer payments landscape.

Leverage Operartis' public benchmark dataset here www.operartis.com/benchrec, a valuable resource for AI reconciliation evaluation.

What's Next?

Don't settle for the limitations of manual reconciliations. Operartis offers a glimpse into the future of financial services with machine learning - a future powered by data-driven insights and AI.

Want to find our more? Read A Buyers Guide to Machine Learning-Based Transaction Matching and transform your reconciliation with AI.

Let us help you transform reconciliations from a burden into a strategic advantage.

Get a quote personalised to your use case with our Proof of Value (PoV) assessment. We'll measure the match rate improvement on your reconciliation data and provide an automation report detailing your ROI before you commit to purchase.

Let's talk it over – schedule your demo to increase efficiency, improve exception management, reduce costs, and enhance visibility into your financial data.