What are Machine Learning Solutions?
Machine Learning (ML) is an analytics and statistical method that, based on many algorithms and large amount of data, teaches computers to imitate the way human beings learn. ML is a large subset categorised under Artificial Intelligence (AI).
ML can be adopted in nearly all aspects of life, from image recognition to healthcare, sentiment analysis to marine wildlife preservation and self-driven cars to content filtering, there is an abundance of applications within this subsector. ML is not to be confused with deep learning, as machine learning generally requires some human intervention when a decision is incorrect, in contrast to deep learning which uses artificial neural networks to establish accurate conclusions without humans being involved.
When assessing this segment, the true question to ask should be, “What can’t Machine Learning do?’.
Machine Learning Explained in 100 Seconds
Global Industry Analysis
According to Forbes, the ML market was valued at USD 19.2 billion in 2022, forecasted to grow to USD 225.91 billion by 2030, with an estimated CAGR of 36.2% across the 8-year period[i]. The North American region was the most dominant worldwide, valued at USD 56.75 billion in 2023[ii]. The Asia-Pacific region is expected to grow much faster than the overall global market, at a compounded annual growth rate of 43.5%[iii]. The global growth in demand can be attributed to large populations, diverse, capital-intensive industries and the forever growing investment in technological infrastructure.
One-fifth of the revenue share within this industry can be tagged to the advertising & media segment, namely due to ML’s ability to enhance personalised adverts, ad fraud detection and cross-channel optimisation, which ML algorithms plan budgets to improve advertising campaigns.
Below, from Grand View Research, we can see a breakdown of the market’s end user segmentation[iv]:
Global Key Players
Many companies have begun to utilise machine learning due to its incredible range of applications, putting them in a competitively advantageous position, its ability to help reduce costs through optimising operations and identifying where costs can be cut, and to boost overall company efficiency by automating jobs that would previously be completed manually, maximising employee bandwidth for more important, strategic tasks.
Amazon Web Services is one of the largest divisions of Amazon that makes use of ML. It is the most successful business segments within Amazon, generating USD 80 billion in 2022 and USD 23 billion in operating profit[v].
Machine learning’s specific models in Amazon Web Services include binary classification models that can predict one of two possible outcomes, a multi-classification model that can predict multiple conditions, used in tracking customers’ orders, and a regression model that can predict an exact value, such as the best-selling price or optimal number of units of a specific product.
Another major company employing machine learning is JPMorgan Chase, who recorded an astounding USD 41.3 billion in the second quarter of this year, with net income rising 149% to USD 14.5 billion. ML is appropriate across the entire banking sector in areas such as sales & trading, operations, risk and finance.
The capabilities of ML in banking are both wide & deep. Machine learning can be used for credit underwriting, fraud detection, cybersecurity enhancement, risk management and document processing. One other point to stress is the ability of AI to conduct task automation. One community bank, Quontic, adopted an ML solution to replace the work of independent auditors. These auditors would spend thousands of billable hours reviewing legal documents to ensure the bank complied with government regulations, however the ML process eradicated the need for this work by scanning documents and identifying the most important points the bank should note.
The ML solution drastically improved Quontic’s overall digital customer experience; between October 2018 and November 2019, Quontic saw a 210% increase in the total number of banking customers, a 100% increase in new personal banking customers, a 261% increase in retail deposits and a 15% YoY increase in funding growth[vi].
UK Industry Analysis
The UK’s ML market troughed at USD 3.25 billion in 2022, but is expected to reach USD 19.02 billion by 2030, forecasting a CAGR of 16.81%[vii]. While this does not grow as much as the global market, the UK is still adamant on expanding their artificial intelligence industry, and machine learning specifically. The British government, from March 2023, plans to GBP 250 million into artificial intelligence “in a bid to secure the UK as a scientific and technological superpower by 2030”[viii]. Specific areas the government wishes to fund include quantum computing and engineering biology; two very established fields with the potential for a huge return on investment.
Below, we can see the UK’s machine learning market value forecast, from Statista[ix]:
Target UK Companies
The United Kingdom has a plethora of early-stage startup companies engaging directly in the design and development of machine learning applications, spanning all industries. Here at JCinus Partners, we have identified and analysed five machine learning startups that could help revolutionise your business.
A series C decision intelligence company that offers data analysis solutions using Artificial Intelligence software. Customers include PrettyLittleThing, KFC and PepsiCo. On average, their customers have seen a 5% increase in total revenue, a 200% return on advertising spend and a 5% reduction in supply chain costs. The Company has raised over USD 100 million across 5 funding rounds.
This series B Company aids recruiters and employers in assessing the behaviour of their applicants by combining game technology, AI and neuroscience to provide data-driven insights to allow less-biased hiring. They have already offered solutions to over 100 customers and their platform has assessed around half a million candidates over the past decade. The Company has received nearly USD 15 million in funding across 4 rounds.
A series B company offering capital market data and analysis solutions for trading participants. Their main customers include banks, brokers, hedge funds, asset managers and academic institutions with order book data and pre- and post-trade analytics. Users can utilise the cloud-based platform for back-testing, benchmarking, risk & compliance measurement, trading efficiency and market impact measurements. The Company has raised ~USD 40 million across 4 rounds of funding.
This Series A startup provides predictive sales analytics to its customers as an alternative to traditional CRM systems by making use of incredibly accurate predictive sales forecasting models. The Company has raised USD 6 million in 2 rounds.
The final company we identified to help investors is a series A startup with the aim of helping businesses manage their datasets. This Company has specifically helped its clients in the industries of healthcare, life sciences, manufacturing, autonomous driving and agri-tech. They have gained USD 43 million across 3 funding rounds.
- [ii] https://www.statista.com/outlook/tmo/artificial-intelligence/machine-learning/united-kingdom
- [iii] https://finance.yahoo.com/news/machine-learning-market-hit-419-104000471.html
- [iv] https://www.grandviewresearch.com/industry-analysis/machine-learning-market?utm_source=prnewswire&utm_medium=referral&utm_campaign=ICT_17-July-23&utm_term=machine_learning_market&utm_content=rd1
- [v] https://fourweekmba.com/aws-revenues/#:~:text=Amazon%20AWS%20(cloud)%20is%20the,%2418.5%20billion%20in%20net%20profits.
- [vi] https://www.itransition.com/machine-learning/banking
- [vii] https://www.statista.com/outlook/tmo/artificial-intelligence/machine-learning/united-kingdom
- [viii] https://www.information-age.com/uk-government-to-invest-250m-into-ai-123501964/
- [ix] https://www.statista.com/outlook/tmo/artificial-intelligence/machine-learning/united-kingdom