Andy Botrill is the VP EMEA for BlackLine.
Trust: a firm belief in the reliability, strength, truth, or capability of someone – or something. By this definition, trust in business has perhaps been waning of late, particularly when it comes to the integrity of financial data (opens in new tab).
In fact, in the UK, a series of high-profile corporate collapses caused by poor commercial management, followed by a trio of major reviews into the audit industry, means that financial (opens in new tab)services is now the country’s least-trusted sector.
It is therefore no surprise that rebuilding trust has become a top priority for CFOs, and a good place to start is by improving trust internally. How do you achieve this? With finance’s number one driving force: data.
- Check out the best personal finance software here (opens in new tab)
Financial data holds the key
Since businesses rely on financial data to inform many important decisions, confidence in the numbers is the first step toward rebuilding trust. A recent BlackLine survey (opens in new tab) revealed that nearly 70% of global business leaders and finance professionals believe their organisation has made a significant business decision based on inaccurate financial data; while over half are not completely confident they can identify financial errors before reporting results.
Not only can tapping into poor data put you at risk of becoming the next front-page scandal and damage your reputation, it can seriously set your organisation back in its efforts to transform longer-term.
As awareness of the risks associated with inaccurate reporting grows, finance professionals are ready to see more accountability in the industry. So, how can they be confident that the decisions being made based on their data are the right ones?
- Check out the best free personal finance software here (opens in new tab)
Building confidence in the numbers
To build real confidence in data, the crucial first step is to ensure the reliability and accuracy of financial information. Over a quarter (26%) of finance executives report they are concerned about financial errors they know must exist, but over which they have no visibility. With trust hanging in the balance, efforts must be focused on resolving these blind spots and unseen errors that undermine confidence in the numbers.
At the centre of these efforts lies real-time, cloud-based process automation (opens in new tab). By automating manual, time-intensive processes, such as transaction matching or journal entries, data not only becomes more reliable, but people can also spend more time on consultative, analytical tasks; in turn adding more value to the business.
Continuous accounting, for example, a model that combines modern finance strategies and cloud technology (opens in new tab), can put an end to high margins of error and deliver faster analysis and increased operational efficiency. With real-time updates, consistent reports, and numerous controls to enforce accountability – departments are beginning to reap benefits of better data integrity and a bolstered culture of trust.
- Check out the best cloud storage services here (opens in new tab)
Transparency: the key to accountability
As technologies such as artificial intelligence (AI) (opens in new tab), machine learning (opens in new tab) and robotic process automation (RPA) (opens in new tab) contribute more toward the decision making process, transparency becomes fundamental. Not only is full transparency critical for effective decision making, but it will also be a significant factor in achieving ‘ethical’ and ‘explainable’ AI.
While finance professionals have always expected to be held accountable for certifying the books, they are increasingly putting their name against decisions made by machine learning algorithms. As a result, it’s never been more important to understand why recommendations are made, or how decisions are reached. Otherwise, how can you ever be confident in the validity of the outcome you are held accountable for?
The need for transparency is only going to grow as technology becomes more imbedded in finance decisions. For example, if an algorithm denies a loan request based on factors such as postcode, it may be inadvertently replicating the bias we see in human decision making. Understanding how the decision is reached, however, would allow a person to review or challenge it, helping to determine the correct, but also ethical, course of action.
- Check out the best in free cloud apps for business here (opens in new tab)
While truly intelligent technology like AI is not a reality for finance right now, it will certainly start to penetrate more tasks and roles within the industry as it advances in future. For now, automation (opens in new tab) in finance ensures that wider business decisions are made based on the most accurate and up to date information available.
Not only does this help to build a culture of trust within the finance department, but the shift towards internal integrity will play a pivotal role in boosting trust in the business more generally – be that with its customers, shareholders or the public.
Ultimately, using automation to enhance the accuracy and reliability of your company’s data will pay dividends further down the line. After all, what use will intelligent AI be if it learns from flawed and inaccurate data sets (opens in new tab)? And more importantly, who would put their name to the decisions or recommendations it makes? The bottom line is that data-driven businesses simply won’t succeed without first laying the foundations of integrity and trust.
Andy Botrill is the VP EMEA for BlackLine (opens in new tab).
- Need webhosting? Find the best services here (opens in new tab)