This guide explores how automation is reshaping the banking industry, addressing key challenges and opportunities in digital transformation. It serves as a practical resource for banking professionals, fintech leaders, and decision-makers looking to understand and implement banking automation solutions.
Digital automation powers banking operations. Where RPA, AI, and ML exist, banks can be more operationally efficient. Those aspects of banking that are repetitive and high-volume—inputting data, account reconciliation, even processing transactions—where human employees were once saddled with tedious tasks are now outsourced to bots who never tire. This means less busy work for banks and more focused access to strategic objectives. Tasks get done for the bank—automated workflows operate between teams with intradepartmental goals, helping avoid bottlenecks and low error rates across massive quantities of transactions to ensure inevitable success.
There's so much crime related to certain payments that one crime can spoil the whole payment system. Banks process millions of payments per day while automatically observing users of various levels, hoping to ensure that nothing is amiss. For decades, banks have counted on an internal employee being able to manually sift through the millions of payments per day that get flagged to legitimize—and deny—potential false-positive transactions. But that's not enough anymore. AI-powered fraud detection allows banks to comb through previous and current payment history in mere seconds. Fraud detection software flags discrepancies and anomalous behaviour to alert banks and clients to fraudulent activity. The rapid growth of this type of illegal activity heightens the reliance of banks and financial institutions on machine learning to identify consistent patterns of fraud—now, criminals are using it to their advantage. Similarly, risk assessment models can determine who will and won't pay back a loan in less than a nanosecond, expediting the loan process for those who qualify and depending upon thousands of data points without human intervention.
Yet expectations for customer experiences transcend industries—online offerings give consumers instantaneous relief—and yet, the bank must help differentiate services by providing better opportunities through automation at every customer interaction. Automated interactions are better. For example, 24/7 AI chat and virtual assistants solve problems and respond to inquiries with empathy and efficiency. Automated onboarding interactions ensure accounts are opened without the necessity of paperwork or human verification, and automated customization via predictive analytics ensures customers receive the proper products precisely when they need them.
While banks carry the burden of a massive labour force and legacy systems, innovation through disruption comes in the form of automation for cost relief. Banks reduce labour costs when they deploy RPA bots in place of repetitive, mundane tasks better suited for machines than human labour. Automated solutions don't get tired, don't take sick days, or vacations, or require benefits, creating a predictable and consistent output at a reduced cost for organizations. Furthermore, with intelligent process automation, reductions in human error and the subsequent need for rework create even more savings. For banks leveraging an automated cloud-based solution, costs associated with an on-premise solution and continual upgrades are avoided as they maintain control over their environment and resources and can leverage automation for savings to reinvest into less expensive solutions.
Compliance is an ongoing battle for banks; new regulations, rules, and laws emerge daily, which means that without manual oversight, the emerging requirements can unintentionally put banks out of compliance, resulting in fines, regulatory audits, and reputational damage. Yet, as noted, compliance can be automated to facilitate avoidance—from oversight of new regulations to automated compliance making internal changes, rules, regulations, policies, and procedures align with the newly emerged requirements. In addition, compliance documentation and audit trails produced by RPA are mistake-free; without human hands gathering and creating files, no human error exists.
Where growth is stifled by legacy systems and manual processing, banks can grow and expand without concern. In the world of banking, consider the growth of a branch with digital banking. When an institution requires more customer service or a revenue-generating opportunity, it's not as simple for them to extend their reach. But with automated, cloud-based processing, this need does not transfer and banks can extend the same. They can use cloud elasticity to process increases in customer service and transaction processing in real time without concern that an expansion will bog down systems. In fact, automated detection of increased service needs will ensure that the backend systems work just as well even if the branch becomes more complicated (more people, more transactions, etc.). Thus, for banks that legacy systems would prevent growth and expansion, this is not an issue.
RPA is the engine of digital transformation. RPA stands for robotic process automation and is a virtual worker type of automated software robot that conducts menial tasks at the machine level—like having a digital workforce—and does so faster and better than humans. In the banking industry, RPA can support these back-end solutions and more—from opening a new account to balancing a transaction. RPA bots take information from customer-submitted forms and input it into requisite databases for compliance reporting; they never get tired (never take a break) and make many fewer, if any, errors.
The engines behind smart banking automation are AI and ML. Where big data is concerned, the human eye cannot perceive such information. These technologically induced advancements find patterns not appreciable by human/non-computing interaction. For instance, with fraud detection, AI scans millions of data points each millisecond to understand correct transaction activity; when something is not aligned, it triggers a warning. In addition, ML offers trained algorithms that acknowledge which patterns are ideal and can adjust natively to those not expected. For instance, with credit scoring, AI can acknowledge that L-shaped data points are one way and R-shaped data sets another when looking from two different perspectives; thus, banks can view social media activity, procrastination in payments as red flags, etc.; this determines risk on loans/credit applications so that banks can make the best decisions with the lowest propensity for defaults/high-return potential for their portfolios.
Blockchain is the trust machine. People can transact without fear of being scammed; there's no need for a middleman because people can trust the machine to perform the necessary due diligence. Simply put, Blockchain is a digital technology that acts as a decentralized ledger and records certain pieces of each transaction in its growing database. The information is kept in an irreversible, encrypted, permanent—and therefore, auditable—form that cannot be deleted. This facilitates banks in trade finance or those with international payment systems.
Cloud computing is the flexible foundation of digital banking development. Banks and credit unions are not limited by resources—as they would be with on-site hardware legacy versions—when they house their systems off-site in the cloud. Instead, the cloud is a digitally always-there, flexible resource management approach that fulfils banking needs without excessive provisioning for potential high-to-low demand.
For example, in minutes instead of days or weeks, developers can spin up minimal test environments instead of spending time on innovative practices; they instead dedicate time to maintenance.
APIs are the secret weapon. Wherever there's online banking, there's an ongoing transfer of data—internally within a banking organization and externally with its collaborators. By exposing the external environment to what a bank does and can provide via using its APIs, a bank can quasi-plug and play everything it needs to bolster and sustain existing offerings. For example, where relevant to the developers, banks can use systems APIs to improve the applications seen by the end consumer, such as the preview of a customer's wealth management screen or more relevant product suggestions.