Cambridge Spark / 
November 17 2022 / 
5 minute read

Machine learning and deep learning have recently taken centre stage in industries like healthcare, retail, banking, insurance, and more – As the global economy has expanded, so has the need for things to be done automatically, meaning  ML is being used to improve the overall efficiency and accuracy of systems in practically every industry. 

Up until recently, the key users of AI and ML in the financial sector tended to be hedge funds, but these days, ML applications have begun to spread to a variety of other fields within the sector. The whole financial industry, including banking and insurance, is moving more and more online. Fintech firms have led the charge, but banks and trading organisation are also increasingly applying machine algorithms to automate time-consuming, monotonous procedures and provide a considerably more streamlined and personalised consumer experience. 

A new survey conducted by Bank of England entitled “Machine learning in UK financial services” found that the number of UK financial services organisations using ML is rapidly growing and looks set to continue doing so. In total, 72% of the organisations polled reported using or developing ML applications, which are becoming more common in a variety of corporate settings. Employers anticipate a 3.5-fold growth in the overall median number of ML applications over the next three years, with the insurance sector predicted to grow the most in absolute terms, followed by banking.

Not only are there a significant number of organisations now using ML applications - many of them are advanced and increasingly embedded in day-to-day operations. According to the same survey, 79% of ML applications are in the final phases of development, meaning they are either implemented across a significant portion of business sectors and/or are crucial to some business areas.  

How does AI and ML benefit the financial services industry?

The scope of use cases for AI and ML in the finance sector are too numerous and complex for the purposes of this article – but the bottom line is that these technologies can increase profits by improving customer service personalisation, reducing costs through more automation, lowering error rates, and improving resource allocation. 

These technologies increase the capacity to analyse and provide insights from massive amounts of data, opening up new and previously unforeseen opportunities. According to McKinsey, the cumulative benefits are so great that the annual potential value of AI and analytics for global banking might be as high as $1 trillion.

To remain competitive, financial organisations need to stay ahead of the curve

In a business landscape where the majority of financial services firms are thinking about ML strategically – according to Bank of England 79% have a strategy for the development, deployment, monitoring and use of the technology. For the 21% without a strategy, they risk losing out on the benefits of ML and decreasing their competitive advantage in an ever competitive industry. It’s a no brainer that to remain competitive, firms need to adopt an AI-first mentality and keep their finger on the pulse of technological innovation by investing in a workforce that has the right skills to build the capability needed. 

There are two ways to do this. One way is to hire individuals with the requisite up-to-date machine learning knowledge and skills – and firms are definitely already doing this. However, finding and adding new talent to an existing workforce can be difficult, costly, and time-consuming given the well documented data skills gap (e.g. last year a report from the Department for Digital, Culture, Media and Sport found that almost half of businesses (46%) have struggled to recruit for roles that require data skills). 

The benefits of upskilling internally

The second option is to upskill existing employees with specialised training programmes, and there are numerous benefits to doing this. Retraining or upskilling often costs less than hiring and onboarding new employees, workforces become more varied and cross-trained as a result of retraining, which in turn increases team productivity. 

Existing employees who have been newly upskilled can also put their new skills to use more rapidly because they already have a thorough understanding of the business and don't need to spend much time on onboarding or gaining domain expertise. 

The specialist data, AI and machine learning training we’re providing to the financial sector

As a leading data science and AI capability partner in the UK, we’re proud to say that we can count multiple US Tier 1 banks, financial regulators, fintechs, quantitative research firms and some of the UK's largest retail banks among our customers. 

Learners who come to us from the financial sector typically report four key business impacts from their new skills: 

  • Deeper data knowledge/skills - Learners want to finish their programmes feeling more authoritative and informed with improved decision-making abilities where data is concerned 
  • Accessibility to data - Learners from the financial sector are usually keen to improve the visibility and access to insights gleaned from data amongst wider team 
  • Financial - Learners are keen to find out how they can apply technology to improve profitability 
  • Risk - There is a great appetite among financial sector learners to learn about methodologies that can reduce risk - e.g. operational, regulatory, technology 

👉 RECOMMENDED READING: The Hidden Costs of Poor Data Quality

What can finance workers achieve with data science and AI training? 

Below we’ve collected some examples of what some of our financial services learners have been able to achieve with the skills they’ve learned on our programmes. To meet confidentiality requirements, we have omitted organisation names and specific project details. 

  • Analysed customer sales data to identify trends in sales that have allowed decisions to be made on more tailored pricing of products, increasing profitability
  • Automated a process of monitoring changes in client data that must be reported to meet statutory obligations, which previously had been done manually, saving the time resource and decreasing errors
  • Built a model to identify applications from clients which are likely to bring more risk - so far has identified 61% of risks without human resource involved, saving the time resource and reducing risks being taken on by the business
  • Automated a process of checking data quality and highlighting anomalies for datasets across the organisation, decreasing the need for manual data auditing

Apprenticeships or bespoke training?

At Cambridge Spark, we offer two different forms of data science training for organisations; government-funded apprenticeship programmes and bespoke commercial training programmes. Here, we’ll break down the pros and cons of each to give an idea of which option could be most appropriate for your financial organisation. 

Apprenticeships

At Cambridge Spark we offer five main data skills apprenticeship programmes. These are the Level 3 Data Citizen apprenticeship, the Level 4 Data Analyst apprenticeship, the Level 4 Data Engineer apprenticeship, the Level 4 Digital Business Analyst apprenticeship, and the Level 7 Data Science and AI apprenticeship

These programmes are funded by the UK government’s Apprenticeship Levy, meaning that companies that pay into the levy can access apprenticeship training at no cost, and non-levy paying companies are only liable for 5% of the cost.

86% of employers said apprenticeships developed skills relevant to their organisation and 78% reported improved productivity.”

- apprenticeships.gov.uk 

Pros of apprenticeships:

  • No/low cost to the organisation
  • Upskill new and existing employees into emerging data science roles giving them the chance to advance their skill set or retrain as a technical specialist working and leading on big data business solutions whilst in full-time employment
  • Improve employee retention by investing in their development with a recognised qualification
  • Increase diversity of thinking within the organisation
  • Emphasis on practical experience and real-world projects to ensure skills and knowledge are applied in your organisation right away

Cons of apprenticeships:

  • Minimum time investment requirement under Apprenticeship Levy funding rules
  • Curriculums are less tailored compared to commercial training options 
  • Eligibility criteria - there are certain rules for funding around residency and prior qualifications, meaning they are not open to all staff to benefit from

👉 RELATED READING: Studying Data Science via an apprenticeship

Bespoke Training Courses:

When it comes to commercial training programmes, we offer bespoke Data Analytics and Machine Learning corporate training which includes tailored courses to upskill staff in Python, data analytics and the latest AI and machine learning techniques. 

Pros of Training Courses:

  • Have no formal eligibility requirements so open to all staff that the employer is looking to invest in
  • The possibility to tailor your organisation’s own curriculum from a range of skills-based corporate training modules focused on applied learning to give your workforce the skills and tools they need most
  • Courses can be structured with the time commitment and duration optimised for business needs

Cons of Training Courses:

  • Without the Apprenticeship Levy, course fees come at the cost of the organisation
  • No official qualification upon completion of training courses

Ready to learn more about our programmes?

Curious to find out more about how Cambridge Spark can deliver gold-standard data, AI and machine learning training to your financial services organisation? Fill out the form below and one of our advisors will reach out with more information.