Tackling data quality challenges in payment reconciliation

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.

Why data quality in payment reconciliation matters

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:

• Enables faster processing: Complete, consistent and accurate data can be easily reconciled without extensive verification or manual intervention. This allows transactions to be processed rapidly.
• Minimizes exceptions and issues: With robust data validation processes, anomalies and discrepancies can be identified early before they create problems in reconciliation. This reduces exceptions that require in-depth research.
• Provides reliable financial reporting: Trustworthy data leads to reconciliation reports that accurately reflect the financial position and transactions of your organization and thereby facilitate reliable regulatory reporting.
• Reduces operational costs: When data quality is high, less manual rework is needed, lowering costs associated with reconciliation. Well-structured data also minimizes IT expenses for maintenance.
• Increases productivity: Clean, consistent data enables automated matching and reconciliation, saving employees time and freeing them to focus on value-adding exceptions and issues.
• Supports compliance: Complete and accurate records help demonstrate compliance with regulations around financial data integrity, transaction transparency and auditing.
• Builds client confidence: High-quality data minimizes potential disputes, improving client retention and demonstrating the reliability of your company’s financial management processes.

By ensuring data integrity from the start, you can ensure that your payment reconciliation activities are effective while also controlling costs and risks.

Common data quality issues in financial processes

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:

• Inconsistent data formats: Disparate systems and lack of standards lead to transactions being recorded differently across business units. For instance, customer names or codes may be captured in multiple formats.
• Missing information: Key fields needed for reconciliation are often missing, such as beneficiary details for payments. Required supplementary data may not be captured.
• Duplicate records: The same transactions end up being recorded multiple times in systems, making it difficult to match and reconcile entries.
• Erroneous data: Data entry errors, faulty automated transfers and system issues result in transactions with incorrect details that don’t reconcile.
• Multiple versions of data: Updates or changes to records may not flow through all systems consistently, creating different versions of the same record.
• Fragmented data across siloed systems: Core financial data gets fragmented across isolated legacy systems and spreadsheets, obscuring the big-picture view required for reconciliation.
• Lack of audit trails and visibility: Inability to track the lineage of data from its origin through various transformations hampers the investigation of reconciliation exceptions.
• Functional silos within teams: Handoffs between different functional teams often lead to inconsistencies or gaps in data context, impacting downstream reconciliation.
• Dependence on manual processes: Reliance on manual entry and handling of data, often through spreadsheets, frequently leads to errors impacting reconciliation.
• Poor data governance: Lack of adequate data standards, policies and accountability create environments susceptible to problems that amplify reconciliation challenges.

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.

The cost of poor data quality on reconciliation efforts

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:

Prolonged processing time

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.

High operational costs

Time-intensive verification and manual resolution of data issues lead to increased staffing needs. ICT infrastructure costs also rise due to increased systems maintenance.

Low confidence in reporting

Data discrepancies and lack of audit trails reduce trust in reconciliation reports. This makes decision-making difficult and raises regulatory compliance risks.

Greater exposure to fraud

Erroneous data enables fraudulent transactions to bypass automated verification checks, while lack of traceability hampers the detection of suspicious patterns.

Inability to scale

Poor data quality hinders the automation of reconciliation processes. It affects the ability to scale reconciliations as transaction volumes increase.

Decline in productivity

Time spent identifying, diagnosing and resolving data problems reduces staff productivity. Low data integrity limits the adoption of efficient digital tools.

Reputational damage

Data issues that delay reconciliations or enable fraud can erode customer trust and lead to dissatisfaction.

Fines for non-compliance

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.

Strategies to improve data quality for reconciliation

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:

1. Establish strong data governance

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:

• Documenting clear standards for data management across departments.
• Designating data steward roles for accountability.
• Developing strong risk management processes and controls.
• Building in high standards across systems via policies and procedures.
• Promoting a culture focused on continuously improving data quality.

With the right governance foundations, you can be proactive and strategic in transforming data accuracy and reliability.

2. Increase visibility through centralization

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.

3. Implement controls for error prevention

With data centralized and standardized, organizations can implement much stronger validation checks and controls:

• Real-time tracking of data lineage helps quickly identify issues before they cascade.
• Automated validations prevent bad data from being recorded in the first place.
• Tools such as Reiterate can help to scan for duplicates or anomalies in real-time in order to minimize errors.
• Dashboards make it easy to monitor data quality metrics across systems.
• Rules trigger alerts for suspicious or fraudulent data so it can be quarantined.
• The ability to embed error and fraud prevention directly into data flows eliminates significant costs down the road.

4. Simplify data transformation

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:

• Resolve duplicate or mismatched records.
• Fill in missing account details.
• Standardize formats and fix inaccuracies.
• Enhance records with additional data for context
• Split complex data into segments for easier reconciliation
• This simplifies reconciliation and ensures teams work only with complete, consistent data.

Ready to improve your reconciliation efficiency?

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.