The vision of joint work between robots and humans is no longer a dystopian or distant view of reality. In fact, every year the predictions of RPA (Robotic Process Automation) involvement swell and grow. The aim is to optimise processes that require the organisation of vast quantities of documentation.
According to Ninety Consulting: “RPA software can be trained to learn specific processes, and automatically handle transactions, manipulate data, trigger responses, and communicate with other systems”.
Next, we will talk about 3 RPA processes that will help your Finance or Insurance business to grow.
1. Modernised customer contact:
Customer contact has undergone huge transitions since the modernisation of digital communication channels. RPA goes beyond simple connection, interpreting using ML (Machine Learning) software the requirements of the clients, complaints or doubts.
This system works by using chatbots, call centre logs, or applications like Aircall that take care of almost all of customer service, saving costs and being available 24 hours a day.
Forbes agrees: “Fewer agents will be required to handle simple queries, and more agents will instead be able to focus on higher-level service interactions that contribute to customer satisfaction, retention, and overall lifetime value”.
2. Fraud detection and prevention:
In many cases, Insurance claims have a fraudulent element representing a high financial loss for the industry. Banks are also exposed to this kind of danger, which is where the automation process comes in. ML (Machine Learning), analyses the huge amount of the customer data, to identify behavioural patterns and finally notice anomalies.
Machine Learning software can also prevent cybercrime from identifying unusual transactions.
McAfee estimates that in the United Kingdom, there is an average of 5.5 million offences annually to commit online fraud and cybercrime.
3. Innovative predictive data sources:
Today, data is at the core of automation processes. Without the right data, Machine Learning software is not going to be precise enough with its predictions. That’s why businesses are using the data provided by the IoT (Internet of Things), including connected devices, cars, homes, social networks, and more, to generate assertive predictions, as potential risks, customer preferences and new market niches.
Check this Forbes article, which lists some big data sources https://www.forbes.com/sites/bernardmarr/2018/02/26/big-data-and-ai-30-amazing-and-free-public-data-sources-for-2018/#61323a55f8ae
References:
- Forbes.com. (2018). 5 Predictions On the Future of Customer Service. [online] Mikhail Naumov. Available at: https://mck.co/2ECfNRb
- Forbes.com. (2018). Big Data and AI: 30 amazing (And Free) Public Data Sources for 2018. [online] Bernard Marr. Available at: https://bit.ly/2GPmBNt
- Gartner.com. (2018). Gartner Top 10 strategic Technology Trends for 2019. [online] Kasey Panneta. Available at: https://gtnr.it/2CJJYGp
- McAfee. (2018). The Economic Impact of Cybercrime — No Slowing Down. [online] Available at: https://bit.ly/2L1jJzN
- Ninety Consulting (2016). The Rise of The Robo Insurer [online] Adam Cranfield and Dan White. Available at: https://bit.ly/2tFjLSP
