Automated Banking For The People

IA consists mainly of the deployment of robotic process automation and artificial intelligence solutions. It enables a bank to acquire the agility and 24/7 access of fintech firms without losing any of its gravitas. With these six building blocks in place, banks can evaluate the potential value in each business and function, from capital markets and retail banking to finance, HR, and operations. When large enough, these opportunities can quickly become beacons for the full automation program, helping persuade multiple stakeholders and senior management of the value at stake. Instead of seeing the results of numerous disparate experiments across the enterprise, these leaders will now see clear transformation opportunities—and be justifiably excited to build the capabilities, systems, and approaches necessary to reach automation at scale. Automation at scale refers to the employment of an emerging set of technologies that combines fundamental process redesign with robotic process automation (RPA) and machine learning.

Barclays introduced RPA across a range of processes, such as accounts receivable and fraudulent account closure, reducing its bad-debt provisions by approximately $225 million per annum and saving over 120 FTEs. Kajal is an Audit & Assurance partner with Deloitte & Touche LLP, based in San Jose, CA. She serves as our west region governance, risk, and controls (GRC) leader within our Accounting and Reporting Advisory business. Kajal brings more than 17 years of combined work experience in external audit, Sarbanes-Oxley (SOX) compliance, internal audit, investment banking and tax advisory at multinational organizations. Kajal has been interviewed on SOX and internal control matters by business journals and has also co-authored various Deloitte thoughtware around GRC.

What Is Banking Automation?

Incumbent banks face two sets of objectives, which on first glance appear to be at odds. On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets.

• Still more have begun the automation process only to find they lack the capabilities required to move the work forward, much less transform the bank in any comprehensive fashion.
• Advanced

  algorithms detect potential fraud, alert relevant parties, and block

  transactions.

• In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates.
• At Softermii, we understand the evolving needs of the financial sector and

  have leveraged our expertise to create impactful fintech solutions.

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AI banking and risk management

Accenture’s 2018 Future Workforce Survey (of 100 banking CEOs and 1,300 bank employees) found, on average, only 1 in 4 senior bank executives is ready to work with AI. Challenger Banks or so-called “neo-banks” are the digitally-first players entering the financial field at the moment. These ‘legacy institutions,’ as the name may imply, are not usually quick to change and, more often than not, have antiquated, siloed data systems that are both costly and cumbersome to run. Data extraction serves a vital function for the vast majority of companies in the financial services industry.

Bank of America, one of the largest financial institutions in the United States, embarked on an RPA journey to streamline its operations. RPA ensures that tasks related to compliance and risk management are carried out consistently and accurately. This is crucial in the banking sector, where regulatory requirements are stringent.

IoT for Smart Banking and Finance: Use Cases, Benefits and Challenges

Key capabilities include managing the release procedure of machine learning models, applying version control to both the models themselves and their training data, and regular review. Financial services customers include US bank PNC Financial, which uses the system to automate approvals for certain loans. The bank combines prescriptive business rules with predictive data modelling to assess applicants’ eligibility for credit, Combs says. Financial institutions have adopted a range of use cases for intelligent automation, from simple integrations of cognitive services into RPA systems to, in a few cases, AI-powered decision making. As such, they have also encountered the security risks and governance challenges that arise from intelligent automation sooner than most.

Automation alone does not simulate human intelligence but rather makes basic processes automatic. Sometimes called intelligent automation, artificial intelligence (AI) and machine learning (ML) algorithms imitate how humans learn and enable better decision-making based on data they have taken in. Although the AI and ML fields are still young, these two are poised to become more relevant to bankers in the future. The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent.

What is Banking Automation?

Developing and applying a strong internal controls framework during due diligence, purchase price allocation or acquisition accounting, and post-transaction integration is fundamental to help meet the expectations of specific stakeholders. Once the acquisition has closed, companies have an opportunity to take a fresh look at their GRC program. For example, misaligning on potential synergies could increase cost to comply long term and be a missed opportunity for long-term efficiencies. These types of transactions are opportunities to refresh and rethink your GRC program. Building out an integration road map that emphasizes efficiency, technology, and automation opportunities can potentially promote growth and collaboration for the combined organization.

Nevertheless, the risks identified by Accenture underscore the need to hold suppliers to account for cybersecurity. “A SolarWinds-type hack on [RPA suppliers] UIPath or Automation Anywhere would be devastating,” says WTW’s Stoeckel. Happily, he says, RPA vendors are “starting to put significant investment into the security layer”. Will advances in robotics, artificial intelligence, and quantum computing make machines so smart and efficient that they can replace humans in many roles today?

Challenges and Considerations

IoT is set to revolutionize the insurance sector, particularly in auto and

health insurance. ‘Usage-based insurance’ or ‘pay as you live’ policies will

become more prevalent. Insurers will use IoT device data, like fitness

trackers and vehicle telematics, to customize premiums according to actual

risk data. According to industry stats, from 2023 to

2029, the IoT market is expected to experience a

compound annual growth rate (CAGR) of 50.1%. Contactless payments are becoming popular, and

devices like smartphones, smartwatches, and wearables make transactions easy.

Automation not only enables banks to achieve operational agility and capture huge cost efficiencies, it also helps them to deliver the digital experiences that customers increasingly demand. As RPA and other automation software improve business processes, job roles will change. As a result, companies must monitor and adjust workflows and job descriptions. Employees will inevitably require additional training, and some will need to be redeployed elsewhere. Merging two companies could bring a host of due diligence, governance, and financial reporting challenges. You will likely face increased expectations of management’s risk assessment and control documentation, new reporting requirements resulting from acquired entity operations, and business goals that have to dovetail with financial reporting objectives.

How Automation Technology Will Influence Banking and Finance in 2022

The bank’s teams used the platform’s cognitive automation technology to perform several tasks quickly and effortlessly, including halving the time it used to take to screen clients as a part of the bank’s know-your-customer process. Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities. That’s why the combination of artificial intelligence and IoT holds vast potential. AI

algorithms can use data from IoT devices to gain valuable insights, predict

market trends, identify fraud, and provide personalized financial advice.

However, only automating back-office processes ignores the true extent of AI’s capabilities. In other words, it’s no longer repetitive manual tasks that are primed for AI technology – there is now a virtually limitless range of applications for intelligent automation. Instead, the primary security risks of intelligent automation are similar to those of RPA. “If malicious code is introduced [to an automated process], it can be amplified multiple times very, very easily,” explains Manu Sharma, head of cybersecurity resilience at Grant Thornton. In particular, access privileges, which are often allocated to RPA ‘bots’ to allow them to conduct certain tasks, must be carefully controlled.

• Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves.
• 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.
• AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns.
• But to prepare yourself for your customers’ growing expectations, increase scalability, and stay competitive, you need a complete banking automation solution.
• They need to wed the redesign of processes and operations to maximize the impact of automation.

Banking automation has become one of the most accessible and affordable ways to simplify backend processes such as document processing. These automation solutions streamline time-consuming tasks and integrate with downstream IT systems to maximize operational efficiency. Additionally, banking automation provides financial institutions with more control and a more thorough, comprehensive analysis of their data to identify new opportunities for efficiency. In another example, the Australia and New Zealand Banking Group deployed robotic process automation (RPA) at scale and is now seeing annual cost savings of over 30 percent in certain functions. In addition, over 40 processes have been automated, enabling staff to focus on higher-value and more rewarding tasks. Leading applications include full automation of the mortgage payments process and of the semi-annual audit report, with data pulled from over a dozen systems.

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