Banking as a Service: Predictions for 2023
Get the new Finextra impact study on Banking as a Service, produced in association with i-exceed to explore how financial
Artificial intelligence (AI) has led to unimaginable innovation in finance, and we have not even scratched the surface. Appzillon is an AI powered digital banking platform which allows banks to understand their customers better and automate mundane tasks. Banks can now provide a personalized experience to their customers and improve engagement with Appzillon’s AI module.
Personalized services, automated processes, fast onboarding, investment advice, chatbots, efficient customer segmentation and many other features powered by Artificial Intelligence (AI) build loyal and long-lasting relationships with customers.
AI in banking can automate repetitive regulatory checks required during onboarding and middle-office functions.
Automating certain tasks which traditionally required manual dexterity does not just save time but also money. According to a report by Autonomous (Financial research firm) financial institutions can decrease operational costs by 22% using artificial intelligence by 2030. According to Juniper research, banking chatbots will save 7.3 billion USD globally by 2023.
AI in banking has revolutionized security with automated Anti-Money Laundering checks and improved video KYC processes. There is a significant reduction in payment frauds and other scams which leads to huge losses.
Appzillon’s AI module contains built-in machine learning (ML) models which can be used for multiple tasks like image processing, OCR, speech to text, omnichannel speech and test search etc. It enables IT teams to integrate ML models seamlessly into their process flows to ensure highly customizable user journeys and reduce efforts involved in data capture with the help of smart screens / conversational interfaces. Appzillon’s AI powered chatbot can easily take care of generic requests from the customers and reduces dependency on human interaction.
The sudden shift of focus from transactions to customer engagement has mandated the banks of today to focus more on user experience combined with an automated/digitized approach towards banking. Artificial Intelligence (AI) enables smooth customer identification and authentication along with chatbots and voice assistants helping build better relationships with the customers. And reduced the hassle of manual processing.
Unlike other industries, banks require solutions that are secure. AI is vital for middle-office functions to detect and prevent fraud and to improve processes for anti-money laundering (AML) and know-your-customer (KYC) regulatory checks. Use of AI Powered chatbots also improves ROI and can manage mundane tasks like balance inquiry, mini statements, transfers, etc.
Earlier every banking customer was exposed to the same products and services due to a lack of efficient customer segmentation. AI enables efficient segmentations of a bank’s customers based on their past interactions, and helps to provide tailormade solutions. Thus, allowing intuitive interactions and improving customer engagement. It also enables banks to send personalized promotions and other notifications, which increases the number of loyal customers.
It can process the digital content (image) and retrieve the information that is present in the digital content. This information can be used by the application for either form filling or business processing like KYC and so on.
Chatbot simulates a conversation with a user in natural language through mobile applications, websites, and messaging applications. Chatbot applications streamline interactions between people and services, enhancing customer experience. Simultaneously, they offer companies new prospects to improve the customer’s engagement process and operational efficiency by reducing the typical cost of customer service. It can be used for Balance check, Transfer amount, Bill payments, Open account, Book an appointment.
Intent Finder also known as Text classification is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content. It is primarily used for customer segmentation and personalized services.
It is an approach used to extract key value pairs from document images like Trade licenses or any other form. It is used for extracting textual information from document images.
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Get the new Finextra impact study on Banking as a Service, produced in association with i-exceed to explore how financial
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