The banking royal commission continues to remain firmly in the spotlight due to the negligence and abuse of trust it is uncovering. Whether it’s lenders carrying out unethical treatment of farmers or bending criteria for home loan eligibility, each week uncovers more evidence of misconduct from many of Australia’s financial institutions.
For the finance industry, not only are these failures a stain on their reputations, they are also costly. Following these revelations, penalties for corporate misconduct will be boosted to include fines of more than $200 million. The government has announced new criminal penalties of up to 10 years' jail and maximum fines of $945,000 for individuals breaching the Corporations Act. Fines for corporations can reach up to 10% of annual turnover.
As a result, the issues of compliance and regulation are now firmly top of mind for any business facing similar pressure to abide by regulatory standards.
Ensuring compliance has traditionally been a tough task due to the sheer volume of interactions that take place between institutions and customers. Not every call between a customer and a contact centre agent can be monitored. As a result, compliance breaches can often slip through the cracks.
Within the industry, it’s well-known that banks and financial organisations, due to resourcing, can only monitor 2-5% of all calls that are taking place in a call centre.
Analysing and evaluating massive data sets is a taxing job for humans, however, machines excel at these kinds of tasks. Spotting and understanding patterns within unstructured data is something AI, when fed the correct information, can do well. As we’ve seen from headline-grabbing products such as Amazon’s Alexa, the applications of AI are growing at an exponential rate and contact centres are in many ways the next frontier of this.
Augmenting how call centres operate
Banks receive an influx of customer calls every day, and although they have systems in place for monitoring certain volumes of them for quality assurance, the banking commission has highlighted the flaws in these systems that have allowed unethical practices to go unchecked. Currently, many businesses rely on managers rating employee performance through restrictive quality assurance scorecards that only assess a certain number of factors.
However, the right technology, such as AI, provides a better solution. AI has the ability to monitor 100% of conversations, which is a vast improvement compared to the limited percentage of interactions that human supervisors are currently able to oversee. All of these interactions are not only recorded; they are also analysed, evaluated, and this analysis can be extended to cover as many factors as needed.
The technology is able to achieve this by mapping word and concept level relationships within conversations and then deducing business specific intelligence and insights. Speech analytics is able to measure everything from the reason the person called to their mood at any stage of the call or contact. AI can link keywords and phrases and carry out semantic matching (which matches phrases on their similarity of meaning).
These machine-enabled insights augment human contact centre agents. By feeding AI-enabled monitoring tools a mixture of information detailing examples of misconduct in call centres, and information outlining correct procedures, businesses can embed new systems that augment the work of employees.
The result is not only an environment that maintains the human touch of call centres but one that establishes systems of self-regulation.
Using AI to reinforce a culture of compliance
When AI programs are installed in call centres to analyse information on what constitutes compliance, businesses can equip themselves with an early warning system.
The observations made by these AI systems can also be repurposed into the training strategies a business uses for new and existing employees. When these AI systems have the capability to pinpoint the exact moments a call centre employee breaks protocol, they also ensure businesses identify what their employees need more training in.
The end result of this is that businesses inject themselves with a new layer of regulatory guidance, a system that clearly identifies when employees break protocol and provide the data to develop tailored training plans that address these occurrences. Beyond training, the analysis of this data provides businesses with new insights that can be implemented across areas like marketing, customer experience and product development.
With consumers demanding more accountability from financial institutions, businesses need to up their approach to compliance and AI tools provide the most effective way to achieve this. In the next two-to-five years having some type of AI-enabled tool to ensure compliance will increasingly be the norm and business which fail to adopt these tools will fall behind. AI will not only improve the customer experience, it will also provide long-term cost savings for Australian businesses.
Article by Daisee CTO Greg Baker