AI-enabled Automation Services Global

On the other hand, intelligent document processing is better suited for ill-defined and unstructured documents. Intelligent document processing uses OCR, machine learning, or deep learning to extract information from various document types. The classic example of RPA is automating customer service tasks and answering frequently asked questions on customer support calls.

Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual process. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., intelligent automation in banking data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish. Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5). Core systems are also difficult to change, and their maintenance requires significant resources.

To Deliver Faster, Personalized Customer Experiences

IPA also promises to enhance efficiency and improve turnaround times and customer journey experiences in ways that are not scalable through normal RPA. O’Reilly has found that many banking institutions struggle with where they can initiate their intelligent automation strategy even when they understand the benefits. In this case, it is critical to start small and focus on the value that can be delivered before deploying intelligent automation across the board.

According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry. By combining automation solutions, such as RPA, with AI technologies such as machine learning, NLP, OCR, or computer vision, financial services companies can move from automating specific tasks to end-to-end processes. PNC Financial Services Group offers a variety of digital and in-person banking services. Examples of IA include robotic process automation (RPA), which uses bots to perform repetitive, high-volume data processes, freeing employees to focus on higher-value tasks. And there’s intelligent capture, the heart of IA, which allows banks and credit unions to capture and classify documents and data. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges.

Blanc Labs’ Banking Automation Solutions

In banking M&As, the consolidation and standardization of financial data are crucial. Automated platforms can harmonize disparate data systems from merging institutions, ensuring seamless integration. They transform complex datasets from different loan trading desks, previously managed in varied formats and structures, into a unified, standardized format. This standardization is key to avoiding data chaos and ensuring efficient, coherent management post-merger. According to testimony given in a webinar from the Institute of Finance and Management, it costs $21 on average to process an invoice manually.

• Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners.
• That includes fraud detection, anti-money laundering initiatives and know-your-customer identity verification.
• The bank also used the intelligent automation platform to expedite its document custody procedures.
• The security boons are self-evident, but these innovations have also helped banks with customer service.

APIs are becoming much more open, functional and capable when it comes to data access. Institutions still on a legacy core system aren’t necessarily stuck — but it will always be more of a challenge to integrate older technology with modern tools. In any case, the key to success is ensuring that the organization finds the right partners and the right solutions to advance the modernization efforts. In a recent live webinar hosted by TELUS International, Ken Mertzel, global industry leader — financial services at Automation Anywhere, shed light on the various ways automation is being used within the banking and financial services industry. While the list of benefits is lengthy, a few of the more prominent use cases are listed below.

Cash management operations

What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases. Without a centralized data backbone, it is practically impossible to analyze the relevant data and generate an intelligent recommendation or offer at the right moment. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way.

Beyond credit scoring and lending, AI has also influenced the way banks assess and manage risk and how they build and interpret contracts. Since then, clients’ customer support expectations haven’t really changed in terms of what they expect, but how they expect them is another story. AI has clearly impacted this landscape, with AI-enabled chatbots and voice assistants now being the norm at major financial institutions. We’re also seeing AI impact biometric authorization and — for those who enjoy the occasional throwback visit to a physical bank — AI-enabled robotic help.

According to a Gartner report, 80% of finance leaders have implemented or plan to implement RPA initiatives. Our avatars will be the ones working overtime, participating in true VR-style collaboration, as well as training and learning modules. Sounds a lot better than getting caught on a Zoom call underdressed, right? Mixed reality technologies will continue to become smaller and more affordable, providing greater collaboration in a hybrid work environment. We dipped our toes in the virtual reality (VR) waters when the pandemic hit, but VR events were awkward and uncomfortable. BankLabs & Participate, pioneering the nexus of fintech and banking evolution.Read Matt Johnner’s full executive profile here.

Many early-stage organizations at this conference seemed destined to follow the same path. They offer a comprehensive view of the combined loan portfolios, facilitating decisions on which loans to retain, sell or restructure. This is particularly beneficial when one of the entities involved in the merger is distressed, and there’s a need to quickly identify and address high-risk loans or nonperforming assets. During M&As, banks need to scrutinize and harmonize their loan portfolios. This process is crucial in identifying loans that may not align with the acquiring bank’s balance sheet strategies, such as those overly concentrated by borrower, geography or asset class.