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 appropriate products and timing for proactive outreach and customization. As a byproduct, second-generation fintech firms also accumulated vast stores of user data, ripe for analysis and innovation.
Leap to AI
Today, firms are beginning to take the leap from first- and second-generation fintech to AI, a giant step requiring the combination of structured data from trading 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 mathematics, statistics, and probability. Back then, it was challenging to find readily available and reliable market data for wealth management, but what was available became a starting point.
Our firm learned that to build out meaningful machine learning, the data sets that feed the system need to represent a reasonable collection of organized, credible, and logical data. Effective machine learning self-corrects, refines, and improves by ingesting, analyzing, and projecting data back out with insights that minimize or eliminate the need for human intervention. In contrast, feeding the system a data set that is neither organized nor logical leads to 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 Softlab360’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.
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