The damaging impact of poor quality data in the financial services sector

It has been estimated by MITSloan that the cumulative cost of inaccurate data is 15 to 25 per cent of revenue for most organisations. This is because poor quality data wastes resources, undermines everyday operations and communications – particularly personalised
customer communications.

It causes inefficiencies in both time and money in the creation and delivery of communications that often aren’t relevant or might not even reach the intended customer. It can, for example, lead to data analysts spending more time trying to sort out data,
and source where the issues are, than analysing it. In fact, according to recent research, analysts can often spend 60 per cent of their time verifying, correcting and reworking data.

Inaccurate data on customers also leads to bad decision making. For example, decision making using poor quality customer data to inform the future of a product or service, or the creation of a new one, will be compromised, with negative implications for
effective resource allocation. What this means in an increasingly AI world is that AI tools are only as good as the data they have access to. If the available data is incorrect or out of date your expensive AI tool will not add any value. In fact, quite the
reverse.

The overall consequence of poor quality data is reduced customer trust in the financial institution they bank with, leading to increased customer churn.

However, it’s not only about losing customers. Having inaccurate customer data will most likely mean that you are not know your customer (KYC) and anti-money laundering (AML) complaint – something which puts your organisation at a greater risk of fraud.

This is why customer data is one of most valuable assets those in financial services have, particularly in a highly competitive financial services marketplace, with fintech and legacy banking giants battling for marketshare. Having clean and up-to-date customer
data is key to remaining competitive and in delivering an all-important single customer view (SCV). This informs personalised communications, and the creation of relevant products and services for customers, thereby helping to drive revenue.

Data decay

Data decay is a big issue that financial institutions face. Customer contact data deteriorates on average at three per cent a month, according to Gartner, and roughly 25 per cent a year, as people move home, divorce or pass away. With data continually degrading
it’s essential to have data cleaning processes in place, not only at the onboarding stage, but to clean held data in batch. All that’s required is simple, cost-effective changes to the data quality regime.

Capture correct data at the customer onboarding stage

It’s always best to obtain accurate contact data at the customer onboarding stage using an address lookup or autocomplete service. These tools provide accurate address data in real-time by delivering a properly formatted, correct address when the user starts
to input theirs. The number of keystrokes required is cut by up to 81 per cent when entering an address, speeding up the onboarding process, improving the whole experience, and making it considerably more likely that the user will complete a purchase or application.
This first point of contact verification can be extended to email, phone and name, so this valuable contact data can also be verified in real-time.

For those without data quality initiatives in place data duplication is a significant issue. Duplicate rates of 10 to 30 per cent on customer databases are not uncommon. Duplicate data adds cost in terms of time and money, particularly with printed communications
and online outreach campaigns, and it can have a negative impact on the sender’s reputation. The answer is to use an advanced fuzzy matching tool to merge and purge the most challenging records and create a ‘single user record’, which helps to deliver a single
customer view (SCV). The insight from which can be used to improve communications. Efficiency savings are made because multiple communication efforts will not be delivered to the same person. Additionally, the opportunity for fraud is reduced with a unified
record established for each user.

Data cleansing and suppression

Undertaking data cleansing or suppression activity to highlight people who have moved or are no longer at the address on file is vital. As well as removing incorrect addresses, these services can include deceased flagging to stop the delivery of mail and
other communications to those who have passed away, which can cause distress to their friends and relatives. Using suppression strategies ensures that financial institutions save money by not distributing inaccurate messaging, safeguarding their reputations,
while enhancing their targeting efforts to overall improve the customer experience.

Source a data cleaning platform

Delivering data quality in real-time to support wider organisational efficiencies and provide a better customer experience has never been easier. A cost effective, scalable data cleaning software-as-a-service (SaaS) platform that can be accessed in a matter
of hours and doesn’t require coding, integration or training can be easily sourced. This technology can cleanse and correct names, addresses, email addresses, and telephone numbers worldwide. Records are matched, ensuring no duplication, and data profiling
is provided to help identify issues for further action. A single, intuitive interface offers the opportunity for data standardisation, validation and enrichment, which ensures high-quality contact information across multiple databases. This can be delivered
with held data in batch and as new data is being gathered. As well as SaaS, such a platform can alternatively be deployed as a cloud-based API, via connector technology like Microsoft SQL Server, or on-premise.

In summary

Poor quality customer data causes a wide range of issues for those in financial services, from incorrect targeting of customers, which delivers a negative experience and increased customer churn, to poor decision making, causing significant inefficiencies
within the organisation. Inaccurate data can also increase the opportunity for fraudulent activity, with KYC and AML compliance not delivered. Putting ongoing data cleaning processes in place is the way forward to provide a standout customer experience to
support revenue generation, efficiency savings and reduce the opportunity for fraud.

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• Source: https://www.finextra.com/blogposting/26280/the-damaging-impact-of-poor-quality-data-in-the-financial-services-sector?utm_medium=rssfinextra&utm_source=finextrablogs