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Client reporting errors can impact clients’ ability to make decisions, erode their trust, and can ultimately lead to fines and sanctions. Ensuring accuracy requires reconciling potentially complex data across multiple systems and ensuring that data is fed to reports without error.

Client information sits in multiple business systems – wherever the client is transacting or has a relationship. To reconcile for KYC checks, your data needs to be normalised, consolidated and fed into your KYC solution, which means hours of work to compare, investigate and correct missing or mismatched data, even when you’ve got a ‘golden source’ reference file.

As you manage the chain of interactions throughout the client lifecycle, data disconnects are bound to happen, leading to errors and potentially lapses in client service. CLM systems help manage this, but they are only as good as the data available to them, so ensuring data quality is vital.

As data flows between systems, enrichment and modifications can allow errors to creep in and propagate across your business. Reconciling this data can be a time consuming and manual process, introducing significant operational risk and limiting your ability to manage change effectively.

Trade data flows through many systems as it moves from execution to settlement and trade data reporting requirements are significant. Reconciling across all phases of the lifecycle can be challenging with many embedded or user developed tools. And yet it’s vital to ensure your data is correct and complete.

When dealing with multiple asset classes and complex instruments the ability to manage margin disputes, reconcile trades, and manage collateral is essential to limiting risk. Even when the data is stored in a risk management or portfolio management system, the complexity can lead to heavy reliance on manual processes and spreadsheets to fill gaps.

When brokers and buy-side firms use different data feeds for corporate actions and market data, inconsistencies can creep into transactions and impact everything from daily cash & securities reconciliations to client reporting.

As data flows between systems, enrichment and modifications can allow errors to creep in and propagate across your business. Reconciling this data can be a time consuming and manual process, introducing significant operational risk and limiting your ability to manage change effectively.

Trade data flows through many systems as it moves from execution to settlement and trade data reporting requirements are significant. Reconciling across all phases of the lifecycle can be challenging with many embedded or user developed tools. And yet it’s vital to ensure your data is correct and complete.

When dealing with multiple asset classes and complex instruments the ability to manage margin disputes, reconcile trades, and manage collateral is essential to limiting risk. Even when the data is stored in a risk management or portfolio management system, the complexity can lead to heavy reliance on manual processes and spreadsheets to fill gaps.

As data flows between systems, enrichment and modifications can allow errors to creep in and propagate across your business. Reconciling this data can be a time consuming and manual process, introducing significant operational risk and limiting your ability to manage change effectively.

Between claims and risk reporting, reinsurers, coverholders and other delegated authorities have to manage thousands of bordereaux reports. Multiple checks are required throughout the process, but manual checks and workarounds are often used to manage complexity and patchy data quality. Getting it wrong can impact everything from claims payments to risk management.

The number of systems and data formats involved in the process can make timely claims payments challenging. Manual workarounds are often used to overcome the lack of standardisation, but these slow down the process and add to total cost per claim. These same data problems and manual processes make payment matching – both premiums and claims – even more of a challenge.

In addition to sanctions and penalties, errors in trade and position information impacts a wide variety of activities including allocations & fees, fiduciary duties and risk positions, just to name a few. But legacy solutions and user developed tools can be inflexible, making managing complex or patchy data from counterparties a challenge.

Fractional pricing, margin requirements and the clearing process can make derivatives data more complex than other securities. You need your data to be complete and correct, quickly, to effectively manage trading and liquidity, client reporting, and regulatory compliance.

With crypto – as with other trades – you need to ensure that your trades and positions are correct between internal records and the blockchain as well as liquidity providers and custodians. However, crypto assets don’t generally have standardised formats for expressing trades and holdings, which can mean a lot of manual work to carry out reconciliations. In addition, the market is demanding more stringent processes and evidence of best practices, which means error-prone manual processes aren’t going to cut it with the regulators or with clients.

Custody data is used to verify valuations, fulfil fiduciary and reporting duties to clients, ensure accurate payments and fees, and understand your risk position. But the volume and breadth of custodians means inbound data is frequently unstandardised and exception management can be clunky and time consuming.

Derivatives clearing is challenging. The data is complex, the volumes are high, timeframes are shrinking and regulatory scrutiny is increasing. Client margin and account data has to be accurate but reconciliations across hundreds of client accounts can mean significant data normalisation issues, that need to be managed at speed to meet tight deadlines for your clients.

Cash reconciliations are a vital and mandatory control. They receive high levels of scrutiny from regulators, and touch almost every area of your operations department. And the nature of them means they require complicated investigations and loads of manual work between multiple systems and stakeholders to identify the root causes of breaks.

Payments reconciliations can be challenging when you’re dealing with many sources and payment providers, all with unique data formats. Disconnected systems, inflexible technology, currency differences and manual workflows can make the process even more complicated.

Trading sheets, sub-ledgers, bank statements, and manual entries all have different formats, presenting challenges for balance sheet substantiation. Ensuring accurate consolidated reconciliation can be a challenge when you’re using inflexible legacy technology, or worse, manual spreadsheet reconciliations as a workaround.

Transaction data needs to be accurate, complete and timely, validated with post-reporting reconciliations. And with refits, rewrites and updates on regulations around the world, you need to ensure the flexibility to adapt those reconciliations when you need to.

Risk teams are busier than ever in the face of a fast-changing business landscape. As processes, systems and activities shift, the complexity associated with compiling, normalising and reconciling risk data continues to increase. Having confidence in your risk exposure and financial health at all times, across all areas of risk, is vital but to do this your teams must deal with large volumes of highly complex data, drawn from disparate sources, in varying file formats.

The risk reporting environment is growing in complexity. In addition to the challenges around risk reporting methods in annual reports, regulations like BCBS 239 and others require timely, accurate and granular reporting of risk data in specific formats. Organisations need to be flexible and adaptable to respond to changing data requirements as well as to changes to the regulations themselves.

When migrating between old and new systems, you need to ensure that your data remains accurate and complete. But frequently, errors can creep in, particularly if you are relying on spreadsheets and existing platforms. Bad data frequently doesn’t show itself until late in the process, when it can have the biggest impact.

Without trustworthy, complete and consistent reference data, errors creep into processes across the business and create mayhem down stream. Reference data impacts nearly every other reconciliation and data management activity your business does. So how do you ensure it’s right?