Artificial intelligence solutions in the finance sector

Artificial Intelligence has taken an important role in analyzing and understanding the behavior of customers and offering them solutions that perfectly match their needs. Solutions provided are classified into two main categories: software tools and platforms.

Software tools

Software tools offer solutions with Artificial Intelligence at the core to cater to requirements in the financial sector. Software tools take data as input and provide solutions as output. They help analyze data patterns and predict the outcomes to follow. Software tools are further categorized into data discovery, data quality and data governance, and data visualization.

Data discovery

Huge amounts of data are generated and gathered in the financial sector. Data discovery software tools can be used to extract this data and apply data modeling and advanced analytics to get useful insights that can help service providers reach out to ideal customers with relevant solutions. This

also helps in fraud detection, stock prediction, and various other utilities.

Data quality and data governance

The huge amount and unstructured form of financial data gleaned leads to several compliance issues. Data quality and data-governance-related software tools prevent data from decay or getting affected by invalid information. The tools also help in structuring, cleaning, and standardizing the data for use in making quality decisions. Data governance tools

are responsible for assigning the ownership of data to compliance-measuring processes and protecting organizations from non-compliance issues.

Data visualization

Data visualization software tools help in the easy interpretation of data. They make the data accessible to all users in an easily consumable and interactive presentation. These tools offer a dashboard through which users can perform multidimensional analysis and segregate the data based on various filters. Graphical presentations include charts, diagrams, and bars.

Platforms

In addition to software tools, platforms are also used to set up AI in Fintech solutions. Many organizations utilize platforms to create solutions that match their exact requirements. For instance, Inbenta offers a customized chatbot platform that enables customers to build customized support bots for Skype and Facebook.

Understanding Conversational Artificial Intelligence

Conversational AI simulates and automates natural conversation through messaging apps, speech-based assistants, and chatbots with an aim to personalize customer experiences. When automation and artificial intelligence are integrated, interactions can connect humans and machines. Conversational AI software enables long-running interactions via text and voice through Natural Language Processing (NLP).

Conversational AI solutions

Nowadays, text- and speech-based platforms have become popular mediums for interactive conversations. Messaging platforms have largely replaced email, voice calls, and face-to-face communication. Communicating on chat platforms is often easier, less intrusive, and faster than other communication channels. Consumers are also increasingly communicating with businesses through text-based chat platforms.

Increasing Need of AI

The increasing use of AI in text and voice messaging will enable the creation of unique conversation experiences and the complete automation of customer interactions. For effective conversational AI, customer experiences must be centered around messaging, chatbots, speech recognition, natural language processing, and artificial intelligence.

Conversational AI lowers customer acquisition costs (CAC) by servicing more customers without increasing staffing costs, offering engaging experiences in more places, boosting overall user experience, and improving collective intelligence. In traditional human-to-human interactions, customers benefit from the experience and knowledge of a single agent. However, with a conversational AI chatbot, customers can benefit from the knowledge and experience gained through every prior interaction ever had. Conversational AI uses several technologies, such as Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog Management, and Machine Learning (ML), to understand and react to human interaction.

Conversational AI solutions also enable users to identify specific metrics that can be improved, such as CAC or LTV. Once the area of business is identified, analysts can focus on the drivers behind them, such as engagement, personalization, novelty, friction, and automation.

Takeaway

Conversational AI offers insight into when, where, and how businesses communicate with their customers across various channels. Using these solutions, management teams can boost user experience across their entire customer base on all platforms, devices, and channels. Once a business perfects the success of its chatbot on a pilot batch of users, it can extend its functionalities for all users and contexts.