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How Synctactic.AI Data Science Platform can help BFSI achieve AI Superpowers

Post by
Suraj Venkat
How Synctactic.AI Data Science Platform can help BFSI achieve AI Superpowers

While customers appreciate the convenience and speed of the financial services being provided to them, the business sector finds it rudimentary to convert their paper-based records to the digital paradigm. Finding a resource that enables companies in the BFSI to check and manage structured and unstructured data, like voice annotations and visual quality inspection, can help maintain and improve the quality of the business. This is where the Syntactic.AI Data Science platform comes into the picture and plays its part in the integrity of the business infrastructure. 

How Do Data Science and AI Help in the BFSI?

Banks, for instance, are leveraging algorithms for friction-free consumer identification as well as authentication, deepen customer relationships, mimic live employees through the help of voice assistants and chatbots, and give personalized recommendations and insights. 

What's more, the technologies in the BFSI work for the KPIs in the sector in certain ways. Some of these ways include:

  • Assessment of risks in middle office functions 
  • Detection and prevention of payment fraud
  • Improvement of the process for AML – anti-money laundering
  • Performance of KYC regulatory checks (Know your Customer)

On the whole, the finance and banking infrastructure with successful strategies undergo AI, and data science enabled transformation to reveal for best capturing the given opportunity. Such ways highlight the requirement for a holistic strategy that extends across the business lines of the banks, usable data, qualified employees, and partnership with external teams. 

Data science induces enterprises in the BFSI to use the data assets that are accessible regardless of the details given of the client, budget, or anything else. If given through a robust and well-functioning platform, it enables the BFSI industry to reach out to the new markets, cross-sell the services and the products via efficient channels of delivery, and improve customer loyalty, for example. Each aspect of the BFSI, specifically banking, be it pricing, risk management, customer and marketing outreach, revenue allocation, and product development cost, the data science finds its ubiquitous applications. 

What is Syntactic.AI?

Syntactic.AI is a data science platform that utilizes innovative tools for transforming your enterprise outcomes. It uses advanced tools to provide leverage to your business with the help of systems and algorithms for extracting knowledge as well as insights from all kinds of data – structured and unstructured. 

How Can Syntactic.AI Improve BFSI?

Analytics from Syntactic.AI can kick start the industry and enable productive ways for success – it helps the industry in becoming smarter when it comes to managing the key business challenges. Let's see what those areas in which Syntactic.AI can help the BFSI enterprises are. 

Fraud Detection

Fraud detection is amongst the pivotal areas of concern for enterprises in BFSI. KPIs such as return on equity and return on assets are in some way or the other dependent on the enterprise's ability to take good care of its security, for example, fraud detection systems. The entirety is covered well through in matters pertaining to insurance, accounting, and credit cards. The system enables proactive detection in this case by providing security for the workers as well as the clients. 

Customer Support

KPI in the BFSI systems pertaining to the quality, such as the Client Survey Score, is deeply related to good customer support. Financial and banking systems can, with the assistance of a well-integrated platform for data science, like Syntactic.AI can get access to different investment patterns, sensitive customer data, and different cycles. As a result, it can help to analyze the customer plans for the future and the credits not in access of the client. Therefore, it can help in presenting the clients with suitable offers. 

Consumer Segmentation 

It essentially means the singling out of the consumer groups that depend on specific attributes or their conduct for consumer behavior division, for example. With the help of decision trees, clustering, and logistic regression, the analytical platform helps to learn the CLV (Consumer Lifetime Value) for each of the consumer fragments. 

Consumer Data

Different banking bodies, management banks, for example, need to compile, analyze, and store large data amounts. The Syntactic.AI platform does not take it as a compliance exercise; rather, it helps the banking and financial bodies to change it into a possibility for becoming well-acquainted with the consumers for driving fresh openings for revenue, another perspective that would increase Return on Assets, for example.

Other properties, like risk modeling and operational efficiency improvement, also come in the solutions Syntactic.AI looks for solving through its well-integrated approach. 

A business can pretty much envision more detailed scenarios through the help of time-series analytics that is not otherwise possible. These include checking call center traffic, volume handling for back-office functionality, advanced planning for assets, and so on. What you get is an enhanced ability for forecasting the future data, which becomes part of the continual transactional and past data. 

Voice Annotations and Visual Quality Inspection

Voice-based options for handling BFSI do not only improve the security aspect of these businesses but also help in checking transaction history, account balances, and other related details. With the integration of voice commands, for example, banking systems can integrate their services. The quality inspection in this aspect remains important to make sure everything is going according to plan and is helping in improving KPI systems. 

Aspects like natural language processing for converting the queries from the end-customers from the voice to text and automating some back-end procedures required for getting precise answers to the customers can be managed well. 

Audio annotation comes as a transcription process for specified intonation and pronunciation and other techniques – for tagging non-speech noise like grass break for emergency and security reasons, and others. 

With most of the projects as seen in analytics, establishing a functional voice-activated product for banking and finance from scratch that enables seamless banking services is quite challenging. It is the main reason why more and more banks are integrating with enterprises like Apple and Amazon for adding voice-activated banking – but this is not the only thing to do. Through the help of a full-scale analytical program like Syntactic.AI, businesses in the BFSI can rightly put these functions to use through empowering better security and growth. Of course, irrespective of the procedures through which the bank functions, analytical programs provide the right resources for product development. It might also control the costs that come with hiring more employees in the customer service team for tackling queries and concerns. 

Henceforth, annotations are usable for pre-processing of text or labeling feature for classifying text – the techniques offer their frames to implement that have variable efficiency for the domain of the application. In this context, an opportunity lies there to investigate as well as apply the language processing frameworks, techniques, and algorithms that suit the annotations in the BFSI system. 

Data-Centric Approach for Better Infrastructure 

For this getting the right data infrastructure for supporting these solutions is a must – the need for better accuracy with more incoming data is always on the increase. A lot of customers might consider it new for activating banking services and interaction channels where the consumer can know about the queries relevant to the organization and what answers they can expect. 

With the help of a customer-centered approach, data science analytical platforms like Syntactic.AI can help build better business models, leverage speech and voice recognition, and other things. It can possibly be imperative for the banks to upgrade the consumer service operations for competing on the same level as the bigger enterprises. The data-first approach is undoubtedly the better way to go for these AI projects. 

As a financial body, there is a high need to upgrade the skills for data leveraging and managing in the best way possible. A common trend that comes in terms of investing from the enterprises has shown why so. For example, we have seen Google has set up the Assistant Investment Program for fund startups that direct towards advancing the speech as well as voice recognition technology. Microsoft and Facebook's partnerships with different enterprises in that reference are also examples of more strengthening of the entire infrastructure. 

The Revolution of BFSI 

The challenges faced by the BFSI system are better solved with the right implementation of an analytical structure built for both structured as well as unstructured data – be it customer experience management or security, the improvements relate to the better KPIs in the business and establish exponential value. 

Final Words

In the current world, the use of data science and AI analytics tool like Syntactic.AI serves as a powerful tool for recognizing the possible flaws, identifying the areas of concern, and augmenting different ways through which the banking or financial enterprise can flourish in the best way possible – an effective way through which banking systems can put AI to good use. 

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