As lenders in the receivables finance space, you’re likely already familiar with the concept of data extraction and its role in streamlining processes.
Yet, despite its clear benefits, adoption among customers remains uneven across the industry. As technology specialists across the full breadth of the lending market, we’ve start to wonder: is data extraction encountering similar barriers to adoption as open banking has in consumer lending?
Exploring this parallel may offer fresh insights into why adoption has stalled and what can be done to overcome these challenges.
The promise of open banking and its struggles
Open banking has revolutionised consumer lending by enabling customers to securely share transaction data with lenders and fintechs. This transparency facilitates better credit assessments, reduces adverse selection, and opens doors for underserved borrowers.
Yet, widespread adoption hasn’t come easily. Issues like consumer trust, data privacy concerns, and inconsistent implementation across platforms have slowed progress.
Despite these hurdles, open banking continues to gain traction. In the UK alone, 76% of consumers now report to connect financial accounts directly to other tools to manage their finances, and 71% said they would now walk away from an online transaction if they have to go to another platform to complete payment.
However, its success has been contingent on addressing fundamental barriers such as building trust in data-sharing practices and ensuring consistent access to high-quality data.
Parallels in receivables finance
The challenges facing data extraction in receivables finance bear striking similarities to those encountered in open banking.
For instance:
- Data quality issues: The receivables finance industry continues to face challenges stemming from both a lack of data, and the mixed quality of the data that is actually available.
Further, many digital accounting systems allow retrospective changes to input data, compromising data integrity and undermining the effectiveness of data integration. - Coverage gaps: Currently only around 80% of accounting solutions are compatible with current data extraction tools in receivables finance, leaving some high-risk customers outside the scope of automated processes.
- Operational challenges: Data extraction technology requires substantial infrastructure to handle increased volumes of data securely and efficiently. For receivables finance lenders, scaling up operational capacity to process significant increases in extracted data remains a significant hurdle. Core platform providers are actively working on overcoming the data volume issue. For us, our role is to support you in filtering and refining this data to highlight key issues to support and continually improve client management.
Another consideration is digital vs. on prem solutions. Online data extraction systems are very stable, but on prem solutions are subject to updates that result in data path, and data structure changes. Resulting in higher maintenance requirements for certain data extractors. This creates a significant challenge for your customers when they are reliant on extractions for their funding. - Incentive: A subtle, but very real barrier to the adoption of data extraction is that of incentive – the ‘what’s in it for me?’ factor among receivables finance customers. Without sufficient knowledge of the benefits to them and how accessible these benefits are, the level of apathy toward fully utilising data extraction will remain unchanged.
Benefits of data extraction for you and your customers
- While there are clear, practical barriers challenging the adoption of data extraction, in some pockets of the market data extraction has been fully integrated into working practices. Certain lenders now have as much as 95% of their book on extraction – illustrating that these barriers are not insurmountable.
In fact, for both you as the lender and your customers, data extraction can be a game-changer, through: - Enhanced risk management: By leveraging data extraction, you can renew underwriting parameters daily rather than monthly. This real-time approach to risk assessment allows for more dynamic and responsive lending decisions.
You're no longer relying on potentially outdated information, but instead have a continuous, up-to-date view of your customers’ financial health.
This enhanced control can lead to more accurate risk pricing and potentially lower default rates. - Improved customer experience: Data extraction enables daily reconciliation cycles, which can significantly enhance the customer experience. Without this technology, customers might be penalised for issues that arise between monthly reconciliations.
With daily updates, these issues can be identified and addressed promptly, fostering a more transparent, fair relationship between you and your customer. - Richer financial insights: The wealth of data made available through extraction opens up new possibilities for financial analysis.
With access to extracted data, Lenvi Riskfactor can create even more sophisticated creditor scores and risk assessments, incorporating a broader range of factors into your decision-making process.
This depth of insight allows for an increasingly nuanced understanding of your customers’ businesses and broader market trends, potentially uncovering new lending opportunities or early warning signs of distress. - Streamlined processes: Data extraction can accelerate live funding decisions, dramatically speed up sales ledger evaluations, and potentially even pre-populate audits and surveys. Not to mention it’s support in risk-based approaches to your risk management, meaning that data extraction could help in identifying the need and frequency of audits and surveys, streamline processing and reduce operational costs.
This not only reduces the administrative burden on your team but also accelerates processes for your customers, creating a more efficient and responsive lending environment. - Real-time funding: Perhaps one of the most tangible benefits for customers is the potential for real-time funding.
With data extraction, invoices can be funded immediately upon approval, rather than waiting for manual processing. This immediacy can significantly improve cash flow for your customers, providing them with the working capital they need precisely when they need it.
These benefits illustrate why, despite the challenges, data extraction remains a compelling proposition for the industry.
What can we learn from Open Banking?
The evolution of open banking offers valuable lessons for receivables finance lenders grappling with customer data extraction adoption.
One key takeaway is the importance of addressing foundational issues like data access and quality early on. Open banking’s momentum grew as lenders and fintechs worked together to standardise practices and build trust among users. Could a similar collaborative effort improve the adoption of data extraction tools?
Another lesson lies in demonstrating tangible benefits to stakeholders. Open banking gained traction by showing how real-time insights could improve credit decisions and expand access for underserved borrowers. For receivables finance lenders, emphasising how daily reconciliations or enhanced risk scoring could directly benefit customers might help overcome resistance.
Concluding thoughts
Ultimately, both open banking and data extraction highlight a broader theme: the financial industry’s reliance on high-quality, accessible data as a driver of innovation.
In receivables finance, better data access could mitigate many of the inefficiencies currently holding back adoption of data extraction but also combat wider fraud risks and operational innovations.
None of the barriers presented for data extraction are impossible to overcome. Conversely, what if these challenges are actually an opportunity? For example, by working with customers to improve their data quality, you’ll likely enhance your relationship with them by demonstrating tangible value. Particularly at a time when some are questioning the long-term appeal of invoice finance solutions.
At Lenvi Riskfactor, we see data extraction as a fantastic opportunity for the development of the industry, and we are actively looking at ways to increase the value that the Riskfactor system can build out more value from data extraction. More on that to follow…
Ultimately, we believe we could be at a tipping point where addressing adoption challenges could unlock the full potential of data extraction. The answer lies not just in technology, but in how we rethink our approach to collaboration, standardisation, and data sharing within the industry.