The Future of Machine Learning in FinTech

Robert Bisewski  

By: Robert Bisewski

The Future of Machine Learning in FinTech

When it comes to financial matters, data is key.

We are collectively and presently experiencing the future of Machine Learning and its impact on FinTech developments. With a $1.41 billion industry expected to grow to $20 billion by 2025, companies can no longer afford the procrastination of digital transformation.

Datasets are the key to unlocking the ability in meeting the standards and growing expectations of customers.

It can mean the difference between retaining and expanding a customer base, especially if a product or service is tailored to their specific needs. With tailored analysis, important and critical trends become visible, allowing a firm to rapidly change to meet market demands.

The market runs on data, and so should you.

Nowadays, many companies are experiencing the perils of Big Data; high hardware cost, slow and complicated software, legacy systems unable to handle the strain, and a general inability to figure out how to turn information into something that could yield edge against the competition.

A number of recent developments in the realm of Machine Learning, notably companies like Google creating TensorFlow and Facebook creating PyTorch, means that Machine Learning is more accessible and easy to use than ever before. This also applies to a number of the latest projects of OpenAI, notably GPT-3, which uses a transformer language model to generate writing from input prompts.

Often in FinTech, companies can find themselves swimming in an ocean of unstructured data that makes it nearly impossible to sift through manually.

It will arrive from a great number of different sources; partial tables with outdated rows, mismatched information between vendors and incomplete historical data. Quite a number of these challenges can be eradicated with the implementation of Machine Learning and AI in their ability to produce actionable data.

Being able to create a simple model that can classify information into something that clients can benefit from, such as the stock and bond composition of a fund, could give a leading edge to brokers looking to replicate the success of other rivals.

A Look into the Future of Customer Support.

Machine Learning alongside AI can greatly benefit today’s firms from the lens of customer support initiatives; chatbots are here and everyone is using them!

Embracing the new paradigm offers a breath of fresh air to customer interaction and relationship building. A client may prefer having a human at the end of every support email and chat message, but traditionally such systems are either greatly expensive or lead to slower and longer wait times.

A notable outcome in using chatbots is improved customer experiences.

Rapid responses, 24/7/365 service, and the ability to save millions of hours for businesses; these are just some of the reasons for using a chatbot.

From FinTech trends to Industry Standards

According to PwC’s 2022 AI Business Survey, companies are advancing with AI in three areas at once: business transformation, enhanced decision-making and modernized systems and processes.

With Machine Learning offering breakthroughs in improved decision making, LOGICLY’s Portfolio Coach makes it simple for an advisor to scale out their portfolio models to more clients in an easy and efficient manner. It’s AI for advisors, creating policies to track model portfolios or customize for asset allocation, cost, risk, income or ESG.

If you are working in FinTech, taking advantage of the exciting prospects in this field will give you a winning edge. Competitors in this space are using Machine Learning architecture and applications, borrowing the best-of-breed in the IT Support industry, in order to enable their offerings to flexibly meet the growing expectations of their clients.

Is your firm ready to include Machine Learning in its overall business plan?

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