Mastering Data Analytics: Seven Proven Strategies for Your Firm
In today's high-tech world, it's no secret that the volume of data generated and collected by...
When disruptive technologies emerge and begin to shift everyday routines, there’s an undeniable and slightly uncomfortable change in the air. Electronic banking, self-managed securities trading, and actionable financial research were all a part of the first wave of technology disruption in financial services, bringing consumers both time savings and convenience. As mobile technology began to emerge, first-generation fintechs used it to bring their ideas and products quickly to market. It was this generation of services that expanded the use of financial products to an even broader range of age groups and demographics.
During the second wave of disruption, fintech firms leveraged both the rapid change and willingness of audiences to sample a variety and differing quality of financial services products, resulting in quick advancement of financial and investment tools at the individual level. During this phase, the focus was on automation and capturing and harnessing data to identify effectiveness.
In the wealth management space, fintechs moved to provide access to a deep breadth of investment instruments while providing immediate access to funds, which enabled banks and lenders to approve and make funds available on the same day.
Leap to AI
Advancements in AI and machine learning now allow for a wider set of options to extract meaning from data, including both structured transactional data and unstructured data.
For instance, integrating portfolio account trades and positions with unstructured data from market reviews and investment strategies and then using this combination to form dialogs and discussions with investors via generative AI. Softlab360 is an innovative software engineering company that applies custom solutions to both electronic trading and wealth management. With expertise in machine learning, Softlab360 offers AI-enabled solutions that fuel strategic initiatives with minimal disruption to operations while actively improving both efficiency and collaboration.
Today, Softlab360 continues its research and development in machine learning through integrations with OpenAI, Application Programming Interface (API), and, recently, with Meta Large Language Model API.
What did the first wave teach?
Since 2014, Softlab360 has been explaining the value of machine learning to customers and prospects. At that time, it was still a vague concept to end-users, yet held a promising future for software developers with a background in discrete math and statistics application methods.
For many years Softlab360 ran machine-learning data exercises on the historical data of stock trading and credit risk issuance. They found that data provided by existing systems was not always of the quality or quantity needed to yield good outcomes, resulting in less-than-optimal predictions.
Since then, Softlab360 has built the tools and strategies necessary to enable customers to qualify appropriate data for machine learning and other uses of AI. This has helped their customers plan for improved data collection in the future. To succeed with AI, second-generation fintech companies must now focus on and improve their automation, self-learning, and machine-learning self-correcting tools.
Applying lessons from the first wave
Lessons learned from creating Softlab 360’s first-generation platform led it to help customers improve their processes for qualifying and selecting appropriate data for machine learning, applying techniques and tools from data science.
To further help with data collection, Softlab360 expanded its staff to include data collection specialists and data scientists who apply proprietary tools to assist customers in conducting data exercises.
In one recent example, Softlab360 applied data management and data learning techniques to a customer’s set of historical data over an 8 to 10-week period. They then shared the results with the customer to assist them in becoming self-sufficient with the best techniques going forward. This incremental approach yielded the best results for adapting AI beyond the current technology and application systems while allowing the customer to see and learn the benefits firsthand.
By extending a helping hand to customers, Softlab360 learned it could help to create optimal results for customers adapting AI systems. As fintech expands and evolves, Softlab360 continues its focus as an industry leader in technologies that make constructive recommendations in investment model management.