Exploring Intelligent Automation in Banking
Banking Automation: The Complete Guide
These new banking processes often include budgeting applications that assist the public with savings, investment software, and retirement information. McKinsey sees a second wave of automation and AI emerging in the next few years, in which machines will do up to 10 to 25 percent of work across bank functions, increasing capacity and freeing employees to focus on higher-value tasks and projects. To capture this opportunity, banks must take a strategic, rather than tactical, approach. In some cases, they will need to design new processes that are optimized for automated/AI work, rather than for people, and couple specialized domain expertise from vendors with in-house capabilities to automate and bolt in a new way of working. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges. Some have installed hundreds of bots—software programs that automate repeated tasks—with very little to show in terms of efficiency and effectiveness.
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Change management principles for automation in banking
The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1). There are many business processes where AI and RPD are already helping financial institutions become more innovative, and plenty of ground remains to be broken. But banks also face numerous challenges when embarking on an automation project. As a result, robotic process automation, cognitive automation (CA) and business process management (BPM) technologies have become key competencies and objectives for most banks.
Not having a unified automation strategy is common, and it creates problems because when you automate something, the workflow often affects the entire firm. Indeed, as banks attempt to put customer needs at the center of their strategies while simultaneously doing away with human jobs, they create a paradox inside their own ecosystem. What to do about this paradox is a complicated question, but what is certain is that whatever action is taken will have a significant impact on future society — one that will belong to Generation Z.
Data in Banking Digital Transformation: key delivery decisions and considerations
A good example, in this case, would be the difference between calling a taxi station versus using a rideshare app to get a ride to the airport. For the best chance of success, start your technological transition in areas less adverse to change. Employees in that area should be eager for the change, or at least open-minded. It also helps avoid customer-facing processes until you’ve thoroughly tested the technology and decided to roll it out or expand its use. Banks can ensure the security of IoT devices and networks by
implementing robust encryption methods, secure data transmission
protocols, and strict data privacy regulations.
The computer program in use was inviting applicants for interviews, but was biased against women and those with non-European names. While the AI was matching the previous human admissions with 90-95% accuracy, the model it was training from was faulty, and so reiterated these biases. Unfortunately, the same could be done in banking and finance with loan approvals and mortgage rates. The AI trains on data you give it, but if the historical data the AI model is trained on has biases, it will match those biases with great accuracy, and thus reinforce the biases.
Automation in banking: 6 considerations for digital transformation
Read more about Automation in Considerations About Technology here.