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Disgruntled customers, mismatching transactions, and risk of fraud — all a number of different but significant risks businesses face. Very often the root cause does not lie in customer services or IT, but instead in the financial department and the mundane activity of payment reconciliation.
What should be a straightforward process is, however, often hindered by poor data quality, different data structures, formats, APIs, and IT systems.
We will show you all the challenges your business may face due to poor data quality and system setups and how to tackle them — by utilizing automated AI solutions such as Reiterate.
Let’s start with the basics – what exactly is payment reconciliation and why should you care?
Payment reconciliation refers to all the processes a company uses to confirm that transactions, transfers, bills, and other payments in their system match up. High-quality data is therefore necessary as the foundation for efficient and reliable reconciliation processes.
A few of the many reasons why data quality in payment reconciliation is of the utmost importance:
By ensuring data integrity from the start, you can ensure that your payment reconciliation activities are effective while also controlling costs and risks.
You and your financial team are probably experiencing some or many of the below on a daily basis. Nasty data quality issues that appear again and again — but you’re not alone, they commonly appear across many organisations:
Robust data management and governance frameworks are essential to address these issues that can undermine reconciliation efforts and Reiterate can help you make the most of data and systems that are not set up perfectly, because of its intelligent mechanisms that learn from your organization's data and get better the more often you use it.
So poor data management clearly creates huge headaches. But what’s the actual impact on reconciliation processes and the business overall? You get stuck with a number of risks:
Data problems like inconsistencies, duplication and missing information drastically slow down payment matching and reconciliation, inflating processing times. This can delay financial close processes and cause compliance issues as well as financial losses.
Time-intensive verification and manual resolution of data issues lead to increased staffing needs. ICT infrastructure costs also rise due to increased systems maintenance.
Data discrepancies and lack of audit trails reduce trust in reconciliation reports. This makes decision-making difficult and raises regulatory compliance risks.
Erroneous data enables fraudulent transactions to bypass automated verification checks, while lack of traceability hampers the detection of suspicious patterns.
Poor data quality hinders the automation of reconciliation processes. It affects the ability to scale reconciliations as transaction volumes increase.
Time spent identifying, diagnosing and resolving data problems reduces staff productivity. Low data integrity limits the adoption of efficient digital tools.
Data issues that delay reconciliations or enable fraud can erode customer trust and lead to dissatisfaction.
Inaccurate books and records arising from data problems lead to violations of financial regulations, resulting in fines.
You can, however, minimize these detrimental impacts through strategies aimed at enhancing data quality across financial functions and taking the problem at the root, making sure spurious data is cleaned up before it enters any key systems such as SAP or your accounting ledgers.
As a responsible finance leader, you can take the following best-practises as a blueprint as to how you can improve your organisation's engine room:
Like any complex challenge, improving data quality starts with people, policies, and culture — not just systems and technology.
Assess your data maturity and develop comprehensive data governance frameworks. This means:
With the right governance foundations, you can be proactive and strategic in transforming data accuracy and reliability.
To fix data consistency issues, you may want to rationalize their complex web of systems onto standardized, centralized platforms.
Consolidating data into a centralized repository or data warehouse improves transparency. Common schemas reduce ambiguity and mismatches. Establishing a single source of truth makes it far easier to manage data accurately as it moves across the business.
Connect legacy systems and decentralize workloads while still maintaining centralized data by using APIs and modern data and system architecture. This balance of flexibility and consistency is the best practice for payment data management.
With data centralized and standardized, organizations can implement much stronger validation checks and controls:
Centralizing data also simplifies the processes of cleansing, enriching, and transforming data into reconcilable formats. That’s where Reiterate comes into play. With our AI-powered solution, you can automatically:
Get in touch with us today for a demo on how Reiterates automated AI-powered software can get your finance department moving and speed up the reconciliation process while improving accuracy and customer outcomes.