As digital transformations and the emergence of neobanking continue to alter the finance industry, data analysis is becoming an increasingly essential component of process optimisation. Consumer data reveals key insights into how customers behave, which allows organisations to provide better solutions and services that match customers’ needs. Likewise, internal data sources can reveal how a company is performing, outlining whether existing efforts (such as their current dunning strategies) are having the desired impact and best serving clients.
Collections data, both internal and external, must therefore be easy to access, correctly formatted and easy to interpret – a near-impossible feat for businesses with legacy systems. This is largely due to a lack of system interoperability, highly manual processes and longstanding data hygiene issues. To achieve maximum value from their data, then, a growing number of companies are seeking to undertake comprehensive data modernisation efforts.
The benefits are wide-ranging; by moving data to a cloud-native system, companies can streamline their data processing and increase their productivity while cutting the cost of system maintenance. Financial leaders will therefore be able to deliver actionable insights and make informed decisions with increased timeliness.
Consider the following points:
- A report by Gartner indicates that approximately 45% of the tech companies who have invested in large infrastructure projects will move from traditional systems to cloud-based systems by 2025.
- According to Deloitte, “The cloud is both a means to and an important consequence of data modernisation”.
- McKinsey statistics show that even with a data-architecture roadmap in place, almost half of banks still have disparate data models.
These figures indicate the importance of data modernisation – though it’s currently still a hurdle for many financial institutions.
Let’s examine why this is, exploring the challenges of data modernisation before diving into key recommendations for collections professionals.
The challenges of data modernisation
Data modernisation is a complex, time-consuming process. Companies have to create detailed modern data-architecture roadmaps, integrate all of their different data models, and ensure their data is accurate and properly ordered.
According to McKinsey, “in banking, while 70% of financial institutions we surveyed have had a modern data-architecture road map for 18 to 24 months, almost half still have disparate data models. The majority have integrated less than 25 percent of their critical data in the target architecture. All of this can create data-quality issues, which add complexity and cost to AI development processes, and suppress the delivery of new capabilities”.
There are 5 key hurdles financial institutions must overcome during their data modernisation efforts:
1. Lack of data quality
Changing data collection strategies and needs, poor documentation, and improper data entry can all have a catastrophic impact on a financial institution’s data quality.
2. Data integration gaps
Financial institutions must be able to effectively integrate data from different sources. However, 38% of surveyed organisations find it difficult to integrate cloud data with on-premise data.
3. Data security issues
Despite its benefits, moving data from on-premise systems to the cloud increases the risk of cyber attacks. Lenders are therefore compelled to implement rigorous data management and cybersecurity protocols.
4. Lack of data and IT talent
Given the complex nature of the transformation process, businesses are usually required to work with well-credentialed data science talent during their modernisation efforts. And despite the increasingly strong job market for data professionals, there remains a significant talent gap in the industry.
6. Inefficient implementation processes
Adopting a new system can sometimes take longer than anticipated. Even once the new solution is up and running, it might fail to deliver the data analysis capabilities and support mechanisms originally promised by the vendor.
Key recommendations for collections professionals
By modernising their data systems, collections departments will find it easier to collect, store, analyse, and derive key insights from their transactions— and from their own performance data.
Though digital-first solutions can foster reduced implementation times and increased efficiency, every solution is different, and collections heads should consider if their vendor can enable the following system improvements:
1. Process optimisation via cloud-based collections
The geoflexible nature of cloud-native solutions eliminates the need for on-premise hardware maintenance and affords teams instant access to up-to-date data – and the software vendor seamlessly handles all updates. Better still, all data stored is a single source of truth, eliminating silos and increasing opportunities for cross-departmental collaboration.
2. The switch to an interactive collections approach
By leveraging the insights gathered from AI and machine learning-driven collections methods, debt management teams are improving the quality of their data and developing more informed recovery strategies at scale.
For modern solutions, this data-driven, iterative approach is principally underpinned by a multi-armed bandit solution, which analyses 2 or more page results in the first instance and uses machine learning to dynamically assign traffic to well-performing variations (for example, dunning email templates). Subsequently, less traffic is allocated to underperforming pages.
Learn more about the multi-armed bandit solution and its role in boosting recovery rates by reading our blog: How the Multi-Armed Bandit Algorithm fuels Collections Success
3. Assured data compliance
Whether according to General Data Protection Regulation (GDPR) or other cross-national data governance regulations, leading collections management software offers compliance as standard. This facilitates better resource allocation and safeguards businesses and consumers alike by reducing data privacy risks.
4. Prioritised business needs
While many vendors will seek to assure clients of a hassle-free implementation process, in reality, an inflexible solution will inevitably result in disruptions to your operation. This is often the result of necessary changes to internal processes to account for the new tool. Further, in some cases, entire operational structures must be redeveloped, to offset a lack of software modifiability.
Your collections solution should be a vehicle for improved process efficiency and not an operational headache – whether at the implementation stage or in system support. In other words, software providers should prioritise flexibility in product delivery, minimising disruptions and fostering better process controls from the outset.
5. A partnership that matches your ambition
While the multitude of market solutions available offer digital approaches to collections, ultimately, long-term value is only achieved through an aligned partnership. That means your vendor should be able to pivot their service to accommodate your long-term ambition: from better data management, to enhanced customer experiences, to full process automation.
A solutions provider that’s attuned to your needs will prioritise your long-term goals and the shifting needs of your business, offering value beyond simple system support.
Data modernisation made easy
Cloud-native software vendors that boast disruptive features, have a strong focus on data compliance, and put their customers’ needs first empower collections departments to undertake data modernisation projects that add tangible value to their operations.
Working with innovative partners that understand and implement new technologies is the optimal way for future-focused businesses to ensure their tech stack moves in lockstep while managing the growing expectations of consumers in a digital lending landscape.
Ready to get started? Book a demo today to learn more about our next-generation data management platform, and discover how to elevate collections in your business – without compromise.