Financial organizations need a solution that allows them to provide intelligent interactions with customers, as well as a friendly way to interact with customers to attract, retain, and provide them with a better experience.
FREMONT, CA: The way people interact with banks and use financial services is evolving. Customers were dissatisfied by the global pandemic as they sought to traverse older digital systems that lacked personalisation and waited in long lines to speak with a customer care specialist. Over the previous ten years, neo and challenger banks have recruited more than 39 million customers, as per the Capgemini World Retail Banking Report 2021. Today, 81 per cent of consumers say that simple access and flexible banking will inspire them to switch to a new-age financial provider instead of their traditional bank. Financial institutions require a solution that allows them to give intelligent customer interactions as well as a welcoming approach to connect with customers to attract, retain, and improve their experience. Machine Learning (ML) is a type of artificial intelligence that is used in Natural Language Processing (NLP). Banks can benefit from machine learning and natural language processing to boost personalisation and flexibility. Chatbots can handle a variety of common client transactions, significantly speeding up the process. Chatbots can quickly determine whether a chat session or a phone caller is looking for information like a balance update or a payment confirmation. NLP can be used in live agent transactions to determine a caller's sentiment and urgency. If a caller is reporting fraud, NLP can detect the urgency of the call and direct the caller to the anti-fraud department. When there is a language barrier between the caller and the agent, NLP for language translation can be used.
According to a recent Forrester report, 84 per cent of technical leaders believe that AI must be integrated into apps to maintain a competitive advantage. Over 70 per cent believe the technology has progressed from its experimental stage and now provides significant corporate value. Developing virtual assistants, chatbots, or messaging apps that give clients information or allow them to receive support from a virtual agent are examples of these solutions. Many financial services firms cannot process and store the large amounts of data generated by machine learning and natural language processing applications. Organizations need faster, more responsible ways to integrate AI and ML into their systems, ideally leveraging their technical team's existing expertise, to make AI and ML a core component of their business. Financial service developers frequently lack the technical expertise required to establish machine learning models that include natural language processing (NLP) to produce custom-designed digital voice assistants (chatbots) that can converse with current or potential consumers. In fact, according to the Forrester study, 81 per cent of technical leaders believe they would employ AI more if it were easy to build and implement.
Microsoft Azure AI is a collection of AI and machine learning services that give users access to high-quality vision, speech, and language models, as well as decision-making models. Using Azure AI and ML capabilities, developers may leverage Jupyter Notebooks, Visual Studio Code, and open-source frameworks like TensorFlow and PyTorch to create organization-specific machine learning models using simple API calls. Microsoft Azure Cognitive Services technology, which has a neural text-to-speech capability and can be deployed anywhere from the cloud to the edge via containers, is available to developers. By delivering built-in AI models and use cases, Cognitive Services makes AI accessible to developers and data scientists. To incorporate the capacity to see, hear, speak, search, understand, and accelerate advanced decision-making into an application, all it takes is one API request.