The financial services industry is full of complex processes, transactions and payments connecting customers, buyers, traders, regulators and other stakeholders. An abundance of legacy systems often leaves high levels of human-dependent process management, making automation crucial to delivering a seamless customer experience. Hyperautomation in particular has emerged as an effective tool for improving efficiency, and in this article, we take a look at the biggest trends involving this technology in finance today.
Actionable integrated data
Much of the finance space often spreads data across isolated silos, but firms have begun to integrate actionable data into one place, from which hyperautomation can thrive, and where a single view of the customer can be realised. This allows finance organisations to monitor and adapt processes in real-time, in line with evolving demands.
“Hyperautomation is about effectively bringing together capabilities including machine learning, process mining, RPA, API integration and intelligent workflow orchestration to replace high levels of complexity with 80%+ automation of the delivery of services to customers. The key to success is actionable integrated data,” explained Keith Pearson, global head of financial services at ServiceNow.
“Fragmented data and isolated systems are the enemy of hyperautomation, and data lake technologies don’t put the data that they hold into the hands of your employees in the workflow. The ability to integrate rapidly to modern and old systems, bringing together process-related data into one place where intelligent automation technologies can be effectively applied is the key to delivering actionable automated workflows and successful outcomes.
“Too many financial services organisations continue to deploy a ‘sticking plaster, hybrid-technology approach’ to achieve their automation goals, inadvertently creating yet more technical debt and islands of data.”
The hottest hyper-automation trends disrupting business today
A key task within finance sectors is the scanning of customer documents, including identity details and bank statements. The amount of data that is at the disposal of organisations in the space as a result adds up, and can be costly, but hyperautomation can help to increase efficiency while minimising costs.
“Financal services (FS) is one of the most data-intensive sectors in the global economy with enormous amounts of customer data to process and analyse for document-based transactions,” said Paul Maguire, senior vice-president EMEA and APAC at Appian.
“The popular means of scanning documents with optical character recognition (OCR) can be really expensive, and is predicted to be a $12.6bn industry by 2025. However, it involves huge amounts of time to set up, as well as humans having to troubleshoot every time a form changes.
“Hyperautomation approaches the same problem in a more efficient way, using robotic process automation (RPA) to bring documents from different sources into the same workflow and using AI to classify and extract information from them, such as checkboxes, and even handwritten notes. When AI detects an error, it can then be automatically offered to a human for validation or correction, and even teaches itself from these interactions to improve over time.”
Another key hyperautomation trend that’s disrupting finance involving the regulatory reporting process, an area that Volodymyr Marchuk, cloud and solutions architect at ELEKS, believes will benefit from automation more often going forward.
“Many banks that we are talking to are already using robotic process automation (RPA) and cognitive intelligence technologies,” said Marchuk.
“This means that manual tasks can be automated 24/7 with limited human supervision. We are seeing improvements in data quality and human workers are able to be redeployed to higher value tasks. However, technologies such as RPA may not be the complete solution for end to end regulatory reporting, and that is where hyperautomation will come in but this may take time.
“Complete automation is often complex and can take years to implement requiring a transformation in the culture of a business.”
The biggest data science trends in banking
Mitigating fraud and errors
Marchuk went on to explain how hyperautomation has proved useful in mitigating fraud and employee errors: “Hyperautomation can significantly reduce financial losses due to fraud, accidents, and errors. According to research from Crowe and the University of Portsmouth’s Centre for Counter Fraud Studies (CCFS), in 2018 global losses due to fraud were calculated to be $5 trillion — 6% of global GDP.
“Hyperautomation, using RPA and machine learning, can solve some of these problems. Using hyperautomation for transaction processing is efficient and transparent, and generated information (action logs) can be used by machine learning for recognition of predictive patters and trends.”
A focus on people
Finally, it’s worth noting that hyperautomation can’t properly succeed without effective management on the part of staff, and this means a need for data democratisation.
Mathias Golombek, CTO of Exasol, explained: “Ironically, one of the biggest hyperautomation trends is actually about people rather than technology. When you commit to a data-driven, hyperautomated environment, one of the natural consequences is a groundswell in the data literacy of staff in every department – and a democratisation of the data that they’re increasingly expected to work with.
“Revolut is one of the field leaders here. The company started as a genuine digital native, authentically data-driven rather than using data as a wider point of reference in decision-making and strategy. It sees hyperautomation as a natural part of managing the hypergrowth that it’s experienced in recent years.
“The organisation has therefore, for example, applied data science to every department, regardless of whether they’re traditionally technological or not. Its HR team, for example, must know SQL for databases: it analyses the interview process, from the questions asked to the correlation of success in a particular role, and use that data to refine the process each time.
“Consequentially, Revolut is both making work-sensitive data far more accessible to staff of every seniority, while also raising the skill floor when it comes to using that data to improve performance.”