Data quality should be a core concern for any business, but for many companies it isn’t – any success that those companies have could often be multiplied many times by attending to their data quality issues.
But for financial institutions, such as banks and insurance companies, the luxury of ignoring data quality is not there. They live or die by the quality of their data – its accuracy, completeness and currency.
Like any business interested in its success and bottom line, financial institutions have a vested interest in improving data quality. Better data quality reduces poor business decisions based on inaccurate and duplicate data; reduces missed revenue and increased costs in repetitive and ineffective processes; allows greater reliance to be placed on financial and business reports; and improves customer satisfaction through correct and timely interaction with them based on full and accurate data.
Yet financial institutions have even greater incentives for achieving the highest levels of data quality. The personal finances held and managed by them are very close to the hearts of their customers, who do not tolerate errors. More than in other industries, a full 360 or single customer view is essential for understanding and managing the interactions, sales and marketing with each customer, and to make sound financial decisions, at the same time allowing sound risk management.
More than this is the high level of regulatory compliance required from the financial sector. Governmental laws, as well as voluntary accords such as Basel II/III and Solvency II, increasingly set stringent requirements which can only be met when data accuracy and quality are high. Institutions are also required to take measures to prevent money laundering, terrorist financing and other fraud.
Just a few days ago the consequences of data quality problems for financial institutions was amply illustrated when a night time process at the ING, The Netherlands’ largest bank, failed and was restarted, causing debits and payments to be made twice. This, together with a tardy response from the bank, caused immense problems. Embarrassed queues of customers at the supermarkets were unable to pay for their food as their accounts had apparently been emptied. Twitter and Facebook glowed incandescently with distress and opprobrium. Questions were asked in parliament and of the financial regulator, and the bank’s CEO had to make a rapid and grovelling apology. This simple error has not only provided the bank with the headache of how to roll back the errors created in its data, it must also manage a massive public relations and regulatory disaster.
Insure your assets
In the insurance industry, collecting non-validated addresses across a number of entry and contact points can create operational problems in various departments, such as underwriting, customer services and claims. Poorly formatted and incomplete addresses can negatively affect processes such as rating, for which location is often an essential component, and customer interaction. Without a complete overview of the customer and their socio-economic and locational information, expensive errors are inevitable. Incorrect addressing causes inefficiency in operations that depend on addresses but do not wait for them to be validated; decreased rating accuracy for each policy holder; lost policy and claim documentation, with the associated costs of rectifying the problems; delays in revenue collection and reduced customer satisfaction.
On another level, banks must make efforts to reduce the damage through lower level crime, such as card and identity theft, and phishing. At contact points, interaction between customer and institution set more onerous security gateways than other businesses need to do, and even relatively routine requests, such as for a change of address, require more work on the side of both bank and customer.
Failures in data quality and governance in the financial sector, and the huge costs and major consequences they bring with them, have been forced into the public eye in the past years, and demonstrate the importance of data quality in every business.
In the finance industry it’s not a matter of choice – without data quality banks and insurers cannot survive.