Digital Transformation in an AI-First World

Every banking customer's journey is an experience in itself, and users expect the best service from the start. Technology plays a critical role in the customer's journey, and the pandemic has increased digital adoption across the globe. Banks and insurance companies have quickly responded with digital transformation to provide customers with a seamless and personalized experience. While the customer engagement part of the digital transformation is the most talked about element in the digital transformation process, there are underlying critical aspects to a successful digital transformation strategy.

Artificial intelligence increasingly plays a focal part in creating value for organizations, customers, and employees. With tech-first companies like Amazon, Apple, Facebook, and Google providing financial services to consumers, there's a significant threat to traditional institutions that do not scale to the next level of digital transformation.

Banks and insurance companies in the first level of digital transformation adopt an Omni-channel approach where their customers can perform the same financial transaction, whether they are on the website, the mobile app, the call center, or the bank's branch. In the next stage of digital transformation, companies need to leverage AI to efficiently automate processes by layering technology, with the focus still being on the customer.

Banks and financial institutions need to change their technology structure and make it AI-first fundamentally to go beyond digital transformation.

Here are critical areas where banks and insurance companies can build AI-first infrastructure to enhance their customers' experience.

Document Enhancement

Millions of physical documents change hands every day, and their digitization is a critical factor in the financial sector. Unfortunately, degradation issues hamper the digitization that impacts the OCR accuracy. Organizations must deploy manual teams to extract the information, leading to delays and additional expenditure. With Visual AI, it is now possible to enhance the quality of scanned documents and make them legible again. Niyogin AI has created a unique AI called SuperScan for auto enhancement of documents. It helps in boosting the accuracy of OCR while saving time & cost on manual intervention.

AI-powered Online Onboarding

Online KYC and onboarding are widely used today as part of digital transformation; however, ground-level issues can impact the customer experience. For example, a customer connecting from a village with weak internet connectivity might not complete the online video KYC due to poor video quality. Super Resolution AI for video enhancement can be deployed in the app to enhance the video in real-time, even in weak bandwidth scenarios.

ICR - Intelligent Character Recognition as part of RPA

OCR (Optical Character Recognition) is a software that scans text and digitizes physical documents in the cloud or on the company's premises. Google Vision, Microsoft Read API, Abby, and Tesseract are popular OCR software available in the market today. The challenge with OCR is that it works mainly on structured documents. To extract data from unstructured documents like handwritten ones, banks are now deploying ICR that recognizes fonts and styles of handwriting and intelligently classifies the text information. RPA companies like Automation Edge have combined OCR, ICR, and AI-Document Enhancement to extract data with higher accuracy across various document types with minimal manual intervention leading to cost savings and better ROI.

Facial Recognition on Edge (Face Captcha)

Facial Recognition is a widely used technology, especially in the post-pandemic world. An advanced Facial Recognition deployed on edge can help provide better security to infrastructure and data. Face Captcha can become the new way of managing access to data even on edge devices without the internet.

AI Chatbot

Customers like the human touch, but they are open to automation as long as the information is personalized for them. For example, AI Chatbots like Yellow.ai help provide a personal chatbot experience to elevate customer service.

Fraud Detection

Customers and employees are increasingly working remotely due to the pandemic, and it is essential to put security checks on the office infrastructure. Face Captcha combined with the detection of Shoulder Surfing can help thwart fraud across ATMs, and workstations at remote locations.

ML Approved Loan Processing

Banks generally depend on a single credit score to evaluate the repayment abilities of the loan applicants. AI and ML models can help in alternate credit scoring methods such as metadata analysis through a smartphone that provides insight into user behavior and spending patterns, decreasing the time to process the loan.

Conversational Intelligence

With AI, it is possible to go beyond traditional sentiment analysis of speech-to-text. One can convert audio and video conversations into text in real-time or after the conversation has ended. Intent analysis of the customer can help in creating follow-up action items. One can also monitor agents' behavior on call and how they talk to customers.

Automated DevOps

DevOps is an impact-driven approach to delivering software, while AI brings in automation. With the combination of AI & DevOps, tech teams can now automate testing and release software in the organizations. AI can further monitor and identify issues and increase collaboration within the units. Automated DevOps can help the banking industry to scale tech integrations.

AI & Blockchain

AI and Blockchain solve different problems but can be combined to improve processes from support, and onboarding to payment processing. For example, opening a stock trading account can take several days because the company needs to collect information from various sources about the customer. All customer information can be stored in a blockchain for AI to quickly analyze and make robust decisions. As a result, financial institutions can offer personalized services to more customers faster and more efficiently.

Building a digital transformation stack for tomorrow requires financial institutions to bring an AI-first vision that joins all aspects of the business while placing the customer at the center. It also needs agility in adopting and leveraging newer technologies enabling a high-speed data streaming channel across functions.

Implement SuperScan AI for Document Enhancement to substantially reduce manual cognitive efforts and empower your workforce to focus on critical tasks for delivering higher business value.

Also read - Top 7 RPA Use Cases and Examples in Banking in 2022