All of our robots receive a time window in which they can run—like 4 a.m. to 11 p.m. We don't specify anything else. We let the machine learning infer when a case comes in. The machine learning algorithm decides how many robots to start, at which time, and when to stop work. Hence, we have no need for having humans monitoring the execution of the robots.Indika Engholm Solution Architect, Danica Pension
The future workforce will be a blend of humans, digital workers, and systems.
One of Denmark’s largest pension companies is already experiencing the future. Blue Prism digital workers now make up 20% of Danica Pension’s workforce. And it is a digital workforce that's getting smarter each day through integrations with machine learning algorithms. Digital workers are so well integrated into the business that having them on the team is part of everyday life for employees at Danica.
Danica Pension was already operating under a model of continuous improvement. And as a part of that model, the company had business objectives that required further savings and optimizations. But unlike the previous round of optimizations, now the processes earmarked for automation were highly complex and much more difficult to convert.
Intelligent automation had been key to achieving maximum efficiency in Danica’s business processes. The automation team was structured, focused on code reuse, and even automated much of their own work. So, while keeping the team the same size (under 15 persons), they grew their digital workforce exponentially. By the end of 2020, digital workers made up 20% of the total Danica Pension workforce. Employees have fully integrated their digital colleagues—it is considered a part of everyday life to have one on the team.
Danica’s digital workforce is getting smarter each day through integrations with machine learning algorithms. The company is diligent about logging information relating to incoming cases from the organization. It knows when a specific type of case is likely to arrive and, using this information along with the machine learning algorithm, can schedule digital workers and allocate work to each. For example, customer inquiries arriving via email needed to be forwarded to the correct department. Danica’s automation team captured the data relating incoming emails, mined the process logs and trained the digital workers to send the mails to the proper recipient.
Digital workers are also adding tremendous value in important areas like anti-money laundering. Digital workers perform ongoing due diligence by looking up the source of funds, validate registration of proper identification and background data for each customer, and produce a report. If an account is deemed low risk the digital worker can close the case. If there are additional questions, the information is passed to a human colleague along with documentation on current status.
Since deployment, the number of hours returned to the business has doubled every year. Last year 250,000 hours were completed by digital workers. And, the digital workforce maintains extremely low operational and maintenance costs regardless of how much the program is scaled.
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