Data error “hot spots” revealed – Financial institutions need to find data errors early

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The ways in which fund data can go wrong are infinite. What matters most is how early errors are detected and corrected, and how this impacts customers, says Stephen Mahoney, executive director at QMV. 

“Achieving error-free investment and customer data is unrealistic, but effective measures can be put in place to reduce the incidence and severity of data errors and to help identify issues early.

“Early identification means before the customer is impacted, which is usually well before the customer makes a complaint and months, or even years, before a large-scale data remediation is imminent.

“A miscalculation, an administrative mistake, lack of insurance coverage, or other errors, can cause customers to feel wronged, robbed, not cared about or even marginalised.”

Mr Mahoney said the task of managing constantly-changing customer data across multiple technology platforms is enormously challenging and the more data there is, the greater the margin for error.

“Nevertheless, customers have an expectation that institutions will hold correct information relating to them, and that it will be used to correctly calculate their financial position and circumstances.

“Understanding the data risks, identifying issues early in the business lifecycle, learning from past mistakes and implementing the correct remediation procedures, will not only benefit each financial organisation but will lead to better customer outcomes. 

Mr Mahoney noted the five most common types of data errors that are encountered.

Fee calculations

“Fee miscalculations and a lack of process controls for documents – such as deeds, product disclosure statements and administrative contracts – are providing the foundation for these errors to occur,” he said. 

Interest crediting

Interest crediting issues relate to direct errors or delay issues giving rise to incorrect calculation of interest / investment returns to customer accounts.

“Delay issues may be caused by a lack of control around standard business processes; for instance, any delay in processing a customer investment switch request could have a large positive or negative impact on customer accounts.”

Eligibility issues

“Eligibility requirements around certain benefits, particularly those related to insurance or credit requirements, can have a huge impact on both customers and the institution. 

“For insurance benefits, these issues are often highly emotive because they involve someone who is hurt or has died, and typically involve large benefit payment amounts,” said Mr Mahoney.

Lack of internal controls

Another example of data error is inadequate controls around the various calculators used for financial decision making, he says. 

“For example, the Royal Commission noted that lack of controls around overdraft facilities led to clients being granted access to funds that they otherwise would not have received.

“This led to the writing off of millions of dollars of overdraft limits, and much bad publicity.”

Lack of critical information

Missing or lost information can cause serious financial errors.

“For instance, if income protection benefits are calculated based on salary, but some employers submitting electronic data for members are not providing salary with their contribution data, then these calculations may be based on incorrect or invalid data and assumptions.”

It is particularly important that errors be identified early and corrected, as when left unchallenged data errors can spread through systems like a disease.

“Constant monitoring of data would ideally be carried out in real-time or as close to real-time as can be achieved. This is particularly important, for example, for exiting customers. Once monies have been paid out, remediation becomes more difficult politically, reputationally and practically, as the organisation no longer has the funds. 

“Data held on administration platforms, advice platforms, CRMs and so on needs to be monitored simultaneously and reconciled against each other.

 “This level of oversight means that customer data is in the best possible condition across all technology platforms, and that costly remediation events are prevented. 

“Organisations that adhere to this level of data maintenance will more easily avoid the data errors that affect their business, and more importantly, affect their end customers,” Mr Mahoney said.

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