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Top 5 Expectations in the Future of Finance in '24

Updated on Feb 14
5 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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According to Forbes, machine learning, blockchain, and omnichannel customer services are some of the most significant financial technologies that will be important in the finance industry. We also think that automation tools and changing the workforce in the finance industry will be important in determining the future of finance. 

For finance executives, it is essential to have predictions regarding the future of finance to decide their investments. Hence, this article explains our 5 expectations about the future.

1. Workload automation industry in finance will grow

The global workload automation (WLA) market is estimated to grow by about 2.2 billion dollars in 2019 and it is expected to grow at a compound annual rate (CAGR) of 6.5% annually from 2020 to 2027 (see Figure 1). 

Figure 1: the estimated WLA market size from 2016 to 2027.

The graph depicts the expected market size of workload automation (WLA) in BFSI, Retail, Healthcare, and many other industries. It is expected to grow by 6.5% from 2020 to 2027. The finance sector is expected to have 25% WLA market in 2027.
Source: Vic.ai

As Figure 1 indicates, the banking, financial services, and insurance (BFSI) sector are expected to increase its share in the automation market from about 20% to about 25% by 2027. Thus, the application of automation tools is expected to grow in BFSI.

The reason for this anticipated increase is related to the fact that finance executives have been attempting to automate general accounting operations (see Figure 2) to focus more on value-added tasks such as empowering decision-making through data analytics to increase their market lead.  

Figure 2: Areas in finance that can be automated.

The illustration depicts how WLA tools can be used in the financial sector. Approximately 80% of general accounting operations and cash disbursement, for example, can be automated. The more strategic the task, the less automatable it becomes. For example, business development cannot be automated.
Source: McKinsey

Hence, in accordance with the data presented above, we anticipate that more finance firms will proceed to automate their transactional tasks such as payroll automation with WLA tools. In this way, companies in the future will be able to focus their employees on tasks that will have a greater impact on their growth, such as deciding on long-term investments.

2. Chatbots will be fine-tuned for use in finance

Financial services are one of the top five industries that benefit from chatbots. Some of the benefits that chatbots offer:

  • Boost internal processes
  • Reduce customer service costs
  • Increase sales
  • And improve customer satisfaction

We expect that financial firms will continue implementing chatbots in their financial services due to their wide range of applications.

We anticipate improvements in chatbot technology to provide more human-like services given that the finance sector will be one of the key investors in the finance sector (see Figure 3).

Figure 3: North America Chatbot Market.

According to the graph, the North America Chatbot Market is currently worth approximately 224.9 million dollars. The finance sector is expected to employ more than 224.9 million dollars by 2030.
Source: GVR

3. Powerful AI will facilitate financial automation

We expect that more powerful AI models will be implemented in the finance sector. 

1. ML will be used in financial services

ML models can improve the quality and efficiency of financial services and increase their security. Hence, we expect machine learning models to be widely used in finance.

2. Synthetic data will be used in finance to train deep-learning models

  • Currently, finding a large size of high-quality data can be challenging to train accurate deep learning models in finance. 
  • We expect that synthetic data technology will be more commonly used in finance to increase data size to increase deep learning model accuracy
  • Hence, we anticipate the use of deep learning to be more widespread in the finance industry.

3. Forecasting will be faster with deep learning models

As Deloitte indicates that the application of powerful machine learning technology operations efficiently can lead to near-real-time processing of data. Finance departments currently rely largely on data analytics for predicting such as market trends.

  • Data analytics takes more time than machine learning technology to process data.

We expect that finance departments will more commonly use deep learning technology for forecasting. 

With the use of deep learning models next to near-real-time data processing, we anticipate that finance companies will be able to have better, faster, and cheaper predictions such as demand forecasting.

4. Blockchain technology will be more commonly used in the finance

1. Currency transactions

Blockchain technology is one of the technologies that is currently accelerating the digitization of the finance industry. The technology offers

  • Improved transparency
  • Enhanced security 
  • Speed in money transfer
  • Reduced transaction costs

In currency transfers. Hence, we expect that blockchain technology to be more widely used in financial transactions.

2. Smart contracts

Also, smart contracts are a new technology that has the potential to improve efficiency in the insurance industry in particular because they are:

  • Cost-effective
  • Secure
  • And Accurate

They yield these benefits because they shorten the time in claims processing and reduce human error factors and the risk of fraudulent claims. Blockchain technology can verify the activation of smart contracts by relying on third parties and this can enhance the security of claims processing further.

3. Internet of Things

Internet of things (IoT) technology can be used by banks to provide personalized recommendations to their customers. Blockchain technology allows for monitoring assets in a more advanced way.

We anticipate that banks will increasingly use IoT technology to provide better services to their customers.

4. NFTs 

The NFT market grew by about 21000% in 2021 and blockchain technology is used in the NFT trades. They can be used in trading fashion or gaming items, or music pieces.

Currently, the market Bitcoin & Stacks, Solana, and Ethereum & Polygon are leading blockchains in the market for NFT transactions. We expect that Bitcoin & Stacks will remain important financial assets for NFT trade because it provides high security. 

Hence, we expect that NFT trade will remain as an important factor in finance with its secure trading.

5. Centralized cryptocurrencies

Central bank digital currencies (CBDC) are currently in the market. These currencies, unlike cryptocurrencies, are created by central banks and they go through a banking system.

We anticipate that central banks will further experiment on CBDCs and potentially some of them to adapt to digital currencies.

5. The employee skills will shift with the automation

McKinsey previously indicated the shift to technological skills set from physical and manual skills and basic cognitive skills in the future of the workforce (see Figure 4). 

Figure 4: The expected shift in the workforce by 2030.

According to the graph, total hours worked with technological skills in the finance sector will increase by 55% by 2030, from 73 billion to 113 billion. On the other hand, the demand for physical and manual skills, as well as basic cognitive skills, is expected to decline by approximately 29 billion hours.
Source: McKinsey

We foresee that the profile of the finance industry’s workforce will gravitate toward a more technologically oriented workforce. With automation tools automating transactional tasks such as revenue management and AI providing near-real-time forecasting with the data, finance companies will focus on hiring automation specialists and machine learning engineers.  

Open Technology Solutions (OPS) can be an example of this workforce change. They were positioning 24/7 hour staff at their data centers for data distribution and other processes to the associated credit unions prior to using the WLA tool OpCon. After the implementation of the WLA tool, OpCon did not require data center employees to troubleshoot problems or integrate data manually. Hence, their 24/7 hour staff at their data centers was reduced to a number of WLA specialists.

Hence, we anticipate that the number of future technologically skilled employees in the finance sector will continue to grow, while the number of employees who use physical or manual skills or basic cognitive skills will decrease. 

To learn more about our expectations regarding the future of finance you can reach us:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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