This is the first in a two-part blog series that focuses on automating and improving data reconciliation. In part one, we will examine the challenges associated with increased automation, the value of improved data reconciliation processes and look to the future of this critical function. In part two, we will cover the role of data integrity as a foundation for improving the performance of the reconciliation lifecycle and how MarkLogic customers are using a data hub to improve reconciliation processes and create business value.
Data reconciliation is a critical component of financial services (finserv) and insurance operations, but the inability to fully automate it is a pervasive challenge with many companies still relying on manual processes. The costs associated with manual reconciliation can total in the millions, making increased automation a critically important operational and financial goal.
At financial firms and investment banks, ever-growing trade transaction volumes combined with the emergence of new financial instruments and greater regulatory compliance demands are putting substantial pressure on reconciliation systems and processes. For insurance carriers, manual reconciliation results in delays for adjudicating claims, resulting in higher costs per claim processed and lower customer satisfaction rates.
The persistent need to continuously improve operational efficiency and contain costs makes improving the reconciliation process more important than ever. More automated reconciliation processes are a key tool for organizations that are seeking to improve precision and speed and reduce costs.
Before we dive into the world of data reconciliation, let’s start with a definition. Data reconciliation describes the method of verifying and validating data collected from multiple systems, and is required to ensure that data is properly consumed by transactional systems while preventing any potential loss of information. The goal of all back-office operations that perform data reconciliation is to reduce manual intervention and exceptions by achieving the highest possible match rates and removing false positives and negatives on processed data, or fully automated reconciliation. Fully automated reconciliation is an attainable goal that has two components:
Even though full automation may be difficult to attain for many organizations, deploying technology to improve data integrity, increase match rates and eliminate manual processes wherever possible is paramount.
Both finserv and insurance organizations deal with two general types of reconciliation: account and transaction. One deals with new accounts, sunsetting accounts, accounts migrated across lines of business and accounts integrated into the collective courtesy of new brokers or agents. Reconciliation of transactions encompasses everything from internal account transfers to documentary-driven settlements for claims, trades, payments and other financial information required to run and report on the business.
With reconciliation still a labor-intensive process, especially when one considers that everything from account information to transaction data and financial records must be matched to their source, organizations find themselves constantly reconciling data.
Not surprisingly, lack of data integrity takes a toll on matching accuracy and the ability to automate reconciliation. Recent research on the United Kingdom insurance industry reveals that only 42.5% of insurers achieve a 90% or higher reconciliation rate for matched premium payment receipts. The remainder have rates between 50% and 90%. With £120bn flowing between policyholders, brokers, insurers, adjusters and other associate parties in the UK, these discrepancy rates create a substantial operational and financial burden for the industry.
Eliminating costly, time-consuming, manual tasks from the reconciliation process is not as easy as it sounds. The primary roadblocks are inaccurate or inconsistent data transmitted by the senders, different file formats and patchwork systems. While standards have helped, many transactions still cannot be automatically reconciled due to miskeyed data or the provision of unstructured data, such as transaction reference data submitted as memorandum or as PDF files.
Paper documents stubbornly remain integral to doing business in the modern era. Whether paying a claim or an invoice, checks and the identifiers on them are data elements critical to account and transaction reconciliation. Even when transmitted, those payment references are not universally standardized, making them difficult to verify and match to an account or transaction.
Another challenge stems from the patchwork nature of many enterprise data systems caused by business mergers and acquisition (M&A) activity. It’s no secret that the financial industries (financial services, including insurance) continue to be among the most active when it comes to M&A. Over the last 30 years, financial industries have accounted for an average of over 30 thousand M&A transactions per year, and represent the largest sector by deal value.
Absorbing entire organizations big or small creates technology obstacles that make integrating data and automating reconciliation seem daunting. Even if it’s just onboarding a new agency or adding a product line, IT departments face headaches caused by multiple systems bolted together, an enduring reliance on paper or antiquated process in multiple business units and even their own legacy tech.
Until those technology roadblocks can be cleared, organizations rely on manual processes. Unfortunately, with only 4% of insurance companies fully automating reconciliation and a whopping 28% relying on wholly manual processes, these roadblocks are often too numerous and too permanent for too many organizations.
Improved reconciliation processes lead to reduced risks and costs and can translate into increased customer satisfaction. Less effort spent on manual processing, research and other forms of intervention saves a significant amount of time and money. According to Fiserv, improvements in reconciliation processes over time can increase productivity savings up to 80%.
Improved data accuracy—and as a result, improved reconciliation accuracy—correlates directly with reducing risk. This can make underwriting easier and more profitable, reduce exceptions and write-offs and help organizations more easily stay in compliance. Resources no longer devoted to manual interventions can be focused on higher-value tasks.
The overall customer experience can be improved as costs are reduced. In the case of filing an insurance claim, customers are clearly happier when claims get processed and paid promptly. There is competitive advantage to efficient, accurate reconciliation, so it benefits organizations to prioritize process improvement through automation. McKinsey found that insurers that digitally transform claims operations through increased use of automation can drive a 20% increase in customer satisfaction scores and a 25% to 30% reduction in claim expenses.
There is a better way. Reconciliation systems are evolving and becoming capable of ingesting multiple formats and unstructured data, handling complex data transformation and reconciliation with intelligent matching capabilities, enabled through machine learning. These systems can STP standard transactions, automate exception management and achieve near real-time posting to downstream systems via STR.
Data integrity is the foundation for these solutions. With it, you can ensure the accuracy and consistency of data across the entire data reconciliation lifecycle from customer onboarding to client servicing and transaction reporting. For insurers and banks, improvements in data reconciliation reduces risk, improves operational efficiency and accelerates decisions—ultimately resulting in a better customer experience.
In the second part of this series, we will look at the role of data integrity as a foundation for optimizing the reconciliation lifecycle and how MarkLogic customers are using a data hub to improve reconciliation processes and create business value.
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