The evolution of the Data & AI space has seen Data, AI, and Machine Learning merge into one landscape. This singular landscape can be broken into many different categories such as open source – the low code and no code companies, source and API companies, businesses that are focused on research and companies that are creating different types of platforms and applications.
Increased funding
In 2020, corporate spending on adopting Data & AI solutions and implementing them into pre-existing infrastructure increased to over $50 billion[1]. When looking at Data Analytics specifically, funding is estimated to hit $25 billion by 2028, and the AI sector is expected to receive $27 billion worth of funding by 2027.
Widespread adoption
According to the FinTech 250, [2] over 90% of FinTech companies currently have a Data & AI component which is now being used more often than not, as the core of any solution. The FinTech 250 goes on to detail that at present, the Digital and Open Banking sector has invested the most in Data & AI solutions, followed by Capital Markets, Crypto and Blockchain, and then Compliance. The RegTech sector alone is expected to invest $22 billion in Data & AI by 2027.
The most commonplace use of Data & AI components in both the FinTech and RegTech sectors is for cyber security, mainly risk assessment and fraud detection. Second to that, 70% of financial firms have adopted the use of Data & AI software to aid with anything from predicting scores in credit analytics to cashflow management.
The FinTech boom
The FinTech space has grown exponentially over the last ten years, and when delving deeper, there are several points that can be identified on the evolution timeline that are key to moulding and shaping the FinTech sector into what it looks like today. The most noteworthy and important advances were the development of Big Data 6-7 years followed by the transition into the AI network 3-4 years ago. Today, both of those solutions have been combined and there is a new focus on Automation. It is also clear that the companies that have implemented and kept up to date with the Data & AI advances have become the most successful.
The importance of Data & AI
There has been a plethora of research conducted and articles produced proving that a company’s success and total funding relate directly to the observability of its data and how well it can process it. It is often a massive factor that sets apart those companies becoming unicorns from those that fail.
The Data & AI landscape has enabled the merging of many different industries that typically couldn’t be aligned or operate together. Open-source technology has made it a lot easier for companies to flow and share their data with one another, and with most companies offering at least one type of SaaS solution, there is a 90% chance that there is going to be a data management and data component to it.
These components have become widely adopted by the whole technology industry, FinTech, WealthTech, RegTech, InurTech and MedTech, and not just among start-ups. Over 92% of Fortune 500 companies have also adopted Data and AI components. Without doing so, they would not be able to keep up with the speed of emerging technologies as their infrastructure wouldn’t be able to support them. If companies don’t adapt now, one or two years down the line, their infrastructure would be so outdated that they would have to completely discard their existing solutions and build new ones from the ground up.
The evolution of these Data & AI components has changed how the technology industry as a whole is hiring and what growth plans look like. There is going to be less of a focus on hiring in the middle office as much of this new software targets middle office operations specifically, taking jobs directly out of this market. The flip side of this, however, is that there will be an increase in demand for back-end and front-end staff – individuals will be needed to create the technology and then needed to drive revenue.
For more information and insights on Data & AI in the FinTech market please contact brendanbray@staging.ec1partners.com
Faqs
Significant advancements in AI and machine learning technologies have fuelled the growth and evolution of the Data & AI landscape. These advancements have enabled practical applications across various industries, going beyond FinTech and RegTech. For example, innovations such as natural language processing (NLP) and computer vision have revolutionised customer service and image recognition. At the same time, predictive analytics has enhanced decision-making processes in sectors like healthcare and retail.
One of the primary challenges in effectively implementing Data & AI solutions is ensuring that the infrastructure supporting these technologies is robust and scalable. Many organisations struggle with legacy systems that may not have been designed to handle the massive volumes of data generated in today’s digital world. These legacy systems often lack the processing power and storage capacity to support advanced AI algorithms and machine learning models.
Moreover, ensuring the quality, security, and privacy of data used in AI-driven analyses poses significant challenges. Data quality issues, such as inconsistencies, inaccuracies, and incompleteness, can undermine the reliability of AI-generated insights. Poor data quality can lead to erroneous conclusions and decisions, ultimately impacting business outcomes.
Security and privacy concerns also loom large in implementing Data & AI solutions. Organisations must safeguard sensitive data from unauthorised access, manipulation, and breaches. Compliance with data protection regulations, such as GDPR and CCPA, adds another layer of complexity, as organisations must ensure that their AI systems adhere to strict data privacy requirements.
Furthermore, ethical considerations surrounding AI, such as bias and fairness, present additional challenges. AI algorithms can inadvertently perpetuate biases in training data, leading to discriminatory outcomes. Addressing these biases requires careful data preprocessing and algorithmic adjustments to promote fairness and equity in AI applications.
The increasing automation of middle office operations through Data & AI solutions has significant implications for the future of job roles within the technology industry. While some middle office roles may be displaced by automation, there will be a growing demand for professionals with data science, AI development, and software engineering expertise. Additionally, there will be opportunities for individuals who can bridge the gap between technology and business, such as data analysts and AI strategists. To prepare for these shifts in the job market, professionals should focus on acquiring skills in areas such as data analysis, machine learning, and programming languages like Python and R. They should also stay updated on industry trends and developments to remain competitive in a rapidly evolving landscape.
