Data is the lifeblood of the finance industry. Consumer data reveals key insights into how customers behave, which allows financial organisations to provide better solutions and services to match customers’ needs. Internal data sources can also reveal how a company is performing, outlining whether existing efforts (such as their current dunning strategies) are having the desired impact.
Collections data, both internal and external, must therefore be easy to access and to be formatted correctly. However, this is almost impossible with legacy systems. To achieve maximum value from their data, companies must undertake comprehensive data modernisation efforts. In other words, they need to move their data from legacy systems to cloud databases.
The benefits are wide-ranging. By moving data to the cloud, companies can streamline their data processing and increase their productivity while cutting the cost of maintenance. Financial leaders will therefore be able to deliver actionable insights, and make informed decisions in a timely manner.
Consider the following points:
- According to Deloitte, “The cloud is both a means to and an important consequence of data modernisation”.
- 42% of financial institutions are currently implementing a data modernisation strategy;
- McKinsey statistics show that even with a data-architecture road map in place, almost half of banks still have disparate data models.
These figures show that despite the importance of data modernisation, it’s currently 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 their different data models, and ensure their data is accurate and well-ordered.
According to McKinsey, “in banking, while 70 percent 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 main hurdles that financial institutions must overcome during their data modernisation efforts:
- 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.
- 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.
- Data security issues
Despite its benefits, moving data from on-premise systems to the cloud increases the risk of cyber-attacks. Financial institutions must therefore work hard to keep their data safe and secure at all times.
- Lack of data scientists/IT talent
Given its complexity, financial institutions need to work with top-tier data science talent during their data modernisation efforts. Unfortunately, such talent can often be difficult to find (and extremely expensive).
- Dissatisfying implementation
Software implementation is often an unsatisfactory process. These projects usually take longer than anticipated (and desired). Even once the new solution is up and running, it might also fail to deliver the solutions and analytical capabilities that the vendor had originally promised.
Key recommendations for collections professionals
Data modernisation is imperative for collections operations. By modernising their data systems, collections departments will find it easier to collect, store, analyse, and derive key insights from their customer data—and from their own performance data.
However, they shouldn’t simply rush into the process without a plan in place. They should instead keep the following five points in mind at all times.
- Adopt disruptive technologies
Collections departments must leverage advanced artificial intelligence (AI) and machine learning (ML)-based software. This will increase the quality of their data, while it will also make it easier to analyse this data—and put it to good use—at scale. For instance, the multi-armed bandit algorithm (MAB) automatically optimises your dunning approach by prioritising your department’s most successful messaging templates.
- Put the cloud at the core of your business
Every collections department needs to use the cloud—it’s as simple as that. Data stored in the cloud is accessible at all times from anywhere, meaning employees can work remotely and effectively. What’s more, all data is stored in a single source of truth—eliminating silos and increasing cross-departmental collaboration.
- Ensure data compliance
Collections professionals must keep a close eye on regional data governance policies (such as GDPR or CCPA). During complex data modernisation projects, they must abide by these policies at all times—respecting not only the law, but also consumers’ right to data privacy.
- Put your organisation’s flexibility first
Large enterprise collection management software companies are often pretty inflexible. Customers have to adjust their systems and their own ways of working to suit the vendor, not the other way around. This has two knock-on effects.
First, it causes unnecessary disruption because companies need to revamp their internal processes just to implement a new tool. Second, everything takes too long. The project grinds to a standstill as collections departments have to go back to the drawing board and recreate their operations from scratch.
Hence, collections departments must work with vendors who put their needs first. In other words, software providers that prioritise flexibility—and are willing to adapt their own ways of working for each client.
- Choose the right tools for the job
By working with cloud-native software that simply layers on top of existing legacy systems, you can speed up your data modernisation efforts. There’s no point in making your data modernisation project harder than it needs to be.
Data modernisation doesn’t have to be a minefield
Collections departments need to derive maximum value from their data. However, this is only possible provided they’re using the latest data architecture, data collection processes, and data analysis tools (such as AI and ML).
This is why data modernisation is so crucial. By partnering with cloud-native collections software vendors that boast disruptive features, have a strong focus on data compliance, and put their customers’ needs first, collections departments can undertake successful data modernisation projects that add tangible value to their operations.