What Are The Most Common Data Errors In Financial Institutions?
Customers expect financial institutions to correctly calculate their financial position and to know exactly who they are. No one wants to be at a loss, especially when it is someone else’s fault.
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.
Achieving error-free data is unrealistic, but effective measures can be put in place to reduce the incidence and severity of data errors by identifying issues early. When I say early, I mean 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.
Below, I outline the five most common causes of data error found in financial institutions such as banks, superannuation funds, insurers and wealth managers. These data error ‘hot spots’ should be monitored especially closely, with tactics put in place to minimise negative impacts.
Top five data error hot spots in financial institutions
1. FEE CALCULATIONS
Misinterpretation between various controlling documents such as product disclosure statements, deeds and administrative contracts typically lead to fee calculation issues. This misinterpretation can occur across several departments; for instance, there may be a different opinion from the legal or risk and compliance department than that taken by the actual business. This is complicated by the fact that there is often no ‘single source of truth’ for business rules; different business units can hold different opinions based on different instruments.
2. INTEREST CREDITING ISSUES
This relates to direct errors or issues involving delay in crediting or calculation of interest to customer accounts, and it occurs quite often. Delay issues may also be caused by processing issues, for instance any delay in processing a customer investment switch request could have a large positive or negative impact on customer accounts.
3. ELIGIBILITY ISSUES
Eligibility requirements around certain benefits, particularly those related to insurance or credit requirements, can have huge impacts on both customers and the institution. Insurance issues are usually highly emotive because they involve someone who is hurt or has died, and typically involve large sums of benefit payments.
4. LACK OF INTERNAL CONTROLS
Lack of adherence to, or inadequate controls around, various calculators used for financial decision making. 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.
5. LACK OF CRITICAL INFORMATION
Missing or lost information can lead to misinterpretation of business rules. 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 different calculations may need to be derived. These calculations may be based on incorrect or invalid data and assumptions.
The scenarios in which data can go wrong are infinite. What matters most is how early errors are detected and corrected. 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.
Reliable detection of data issues requires a dedicated data quality software platform like Investigate. Using Word documents, SQL scripts or Excel spreadsheets to check data is archaic and high risk. Software tools must be able to monitor the quality of all your data, all at once.
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 costly remediation events are prevented, meaning customers stay happy…or happier.
Stephen Mahoney – Executive Director
If your organisation needs assistance with data remediation and data quality management, QMV can help.
QMV have performed hundreds of data remediation projects. We utilise an innovative and flexible approach to assist clients identify and create visibility of data quality issues.
QMV’s extensive work in data quality management led to the development of Investigate our data quality software solution. QMV identified the need for rigorous and systematic data quality management in financial services because poor data quality costs financial institutions millions each year.
QMV provides independent advisory, consulting and technology to superannuation, wealth management, banking and insurance organisations.
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