Artificial Intelligence requires significant data, building and deploying AI and machine learning systems requires significant data sets. Creating key machine learning algorithms is dependent on large volumes of data. To expand and deepen the results and findings made by the algorithm, machine learning requires data from a range of sources, in various formats and from a variety of business processes.

At the same time, AI itself can be vital in determining and preparing the data required to drive the further value of AI and analytical systems. Businesses require more data scientists and specialised analysts to integrate the necessary AI and machine learning algorithms. 

A new era of enterprise analytics is developing and it involves a combination of automation and contextual information. AI-focused analytical systems can develop vital insights and information that can be passed onto decision-makers without requiring specialist analysts to prepare the data. Business intelligence analysts and other data professionals will still play an important role, but many will not be needed to provide added support to other team members and data users.

Smaller businesses that don’t necessarily have the budget for data scientists will be able to measure their data with better accuracy and clearer insights.

The potential to efficiently automate data tasks is dependant on the industry and overall circumstances. Often, there is a need for adequately trained human support for AI and machine learning plans, especially if the output is critical to the business.

While automated AI data science tools can be simple and effective, they may leave businesses with unanswered questions. If you don’t have a background in data science or machine learning, you may not be capable of determining the results or implementing the suggested changes, which can be challenging and time-consuming.

There is the potential to automate certain parts of a data scientist role, but the skills of a data engineer will continue to be a vital asset to an organisation. Data engineering is required to produce smart and intelligent information that can enhance predictive accuracy and support detailed business analysis.

There may be ways to automate various pieces of data science roles, but the skills category that will still be essential is that of a data engineer. There are many tasks required to source, manage and store data in which data scientists don’t necessarily want to get involved. “To succeed with AI, companies should have an automation environment with reliable historian data,” a McKinsey report explains.

Then, companies “will need to adapt their big data into a form that is amenable to AI, often with far fewer variables and with intelligent, first principles-based feature engineering,” the study’s authors, led by Jay Agarwal, state. Data engineering is needed to produce “smart data” to improve predictive accuracy and aid in root-cause analysis. This, along with equipping staff with the right skills, can provide services that can help increase revenues up to 15 per cent, they relate.

Data engineering is vital. A data scientist can’t discover or utilise information until there is a good set of data to work with. Data scientists and specialised data analysts will continue to be in demand and will remain important in supporting businesses to design and test algorithms and data that can determine trends, automate processes and engage with customers. The challenge, however, is the volume of data flowing into businesses and the rising demands for new algorithms and capabilities with data. AI is unravelling a new path to a more effective and accessible AI.

July 28, 2021 by Mike Jones

Artificial Intelligence requires significant data, building and deploying AI and machine learning systems requires significant data sets. Creating key machine learning algorithms is dependent on large volumes of data. To expand and deepen the results and findings made by the algorithm, machine learning requires data from a range of sources, in various formats and from a variety of business processes.

At the same time, AI itself can be vital in determining and preparing the data required to drive the further value of AI and analytical systems. Businesses require more data scientists and specialised analysts to integrate the necessary AI and machine learning algorithms.

A new era of enterprise analytics is developing and it involves a combination of automation and contextual information. AI-focused analytical systems can develop vital insights and information that can be passed onto decision-makers without requiring specialist analysts to prepare the data. Business intelligence analysts and other data professionals will still play an important role, but many will not be needed to provide added support to other team members and data users.

Smaller businesses that don’t necessarily have the budget for data scientists will be able to measure their data with better accuracy and clearer insights.

The potential to efficiently automate data tasks is dependant on the industry and overall circumstances. Often, there is a need for adequately trained human support for AI and machine learning plans, especially if the output is critical to the business.

While automated AI data science tools can be simple and effective, they may leave businesses with unanswered questions. If you don’t have a background in data science or machine learning, you may not be capable of determining the results or implementing the suggested changes, which can be challenging and time-consuming.

There is the potential to automate certain parts of a data scientist role, but the skills of a data engineer will continue to be a vital asset to an organisation. Data engineering is required to produce smart and intelligent information that can enhance predictive accuracy and support detailed business analysis.

There may be ways to automate various pieces of data science roles, but the skills category that will still be essential is that of a data engineer. There are many tasks required to source, manage and store data in which data scientists don’t necessarily want to get involved. “To succeed with AI, companies should have an automation environment with reliable historian data,” a McKinsey report explains.

Then, companies “will need to adapt their big data into a form that is amenable to AI, often with far fewer variables and with intelligent, first principles-based feature engineering,” the study’s authors, led by Jay Agarwal, state. Data engineering is needed to produce “smart data” to improve predictive accuracy and aid in root-cause analysis. This, along with equipping staff with the right skills, can provide services that can help increase revenues up to 15 per cent, they relate.

Data engineering is vital. A data scientist can’t discover or utilise information until there is a good set of data to work with. Data scientists and specialised data analysts will continue to be in demand and will remain important in supporting businesses to design and test algorithms and data that can determine trends, automate processes and engage with customers. The challenge, however, is the volume of data flowing into businesses and the rising demands for new algorithms and capabilities with data. AI is unravelling a new path to a more effective and accessible AI.

Financial services businesses within the UK are becoming more reliant on scraping alternative data sources, with over 60% using alternative data to improve their decision-making process. The new report ‘The Growing Importance of Alternative Data in the Finance Industry’ by Oxylabs highlights the significant rise in web scraping for alternative data over the last year or so.

Over 200 senior data decision-makers in the UK finance industry were interviewed on their existing approach to data management. The findings indicated that web scraping and financial transitions were the most popular sources of alternative data for financial services organisations. This includes non-traditional data sources that may not have been assessed before, such as social media posts, website traffic and other data sources. Conventional sources like official public data and third-party data are still considered valuable but have been overtaken by the significant rise in alternative data.

Julius Cerniauskas, the CEO at Oxylabs, explains that the rise in online alternative data sources has created a sharp increase in demand for web scraping services from financial organisations looking to tackle the challenges from the pandemic.

Cerniauskas states that they have experienced a surge in inquiries from businesses in the financial services industry over the last year, so he explains that they are motivated to learn how these organisations were approaching data collection and analysis.

Alternative data can be implemented to gain a better understanding of business performance, market trends and future investment plans. Financial services businesses can transform alternative real-time data into clear, actionable insights that are far more likely to report significant improvements in decision-making.

Business leaders in the finance industry are continuing to explore new ways of improving investment decisions and reducing risk to their business, so it’s understandable to see that the global alternative data industry is growing and predicted to continue increasing over the next few years.

Looking at the research, it’s clear that financial services businesses are increasingly looking to utilise alternative data to gain more value and discover new insights into performance, industry trends and potential investment opportunities. Data-focused organisations will be in a stronger position to convert this valuable information into actionable insights and deliver strategic decisions in a post-pandemic economy.

Open banking is the transfer of financial data and a trend that inevitably is likely to increase. While open banking empowers fintech and larger technology businesses, a report by McKinsey suggests several significant benefits for established banks and financial organisations that may have not been previously recognised.

Industry experts highlight how our world is changing and how important information is to businesses. Customers are not willing to accept a particular service or price if they are aware of better options elsewhere. Several reports from McKinsey suggest that embracing open banking is necessary and has benefits for financial institutions as much as it had for fintech and other businesses. 

The findings indicate a value to the adoption of open financial data, an increase in GDP of between 1% and 1.5% within the U.S, the U.K. and the EU.

While it’s not exactly clear how open financial data will progress, the trend towards data sharing between financial institutions, fintech and other big businesses is only going to increase. This will have a significant impact on the traditional banking industry shortly.

In the report ‘Financial Services Unchained’, McKinsey explains that if open finance continues to accelerate it could transform the global financial services system and change the concept of banking altogether. The report goes onto say that the ability for customers to gain a deeper understanding of their finances could result in margin compression, as charges and pricing becomes more transparent. McKinsey explains that banks may have to compete with margin sharing, as payouts to other digital platforms could play a bigger role in customer acquisition.

McKinsey also highlights that open financial data places big technology businesses in a stronger position to become financial services leaders. We are increasingly seeing more big tech businesses entering financial services, using open data as part of their offerings. It’s worth remembering that multiple businesses are capable of using the same data and as a result, big technology businesses will have banking partners and will continue to face several banking competitors.

Increased competition will ultimately lead to the need to understand and respond to new changes, restructure offerings, adapt business models and establish partnerships with fintech or technology businesses to drive continued success and relevance.

The benefits and value of open data

While it may sound like conventional financial businesses may face a challenging future, the report ‘Financial Data Unbound’ by McKinsey details several benefits of open financial data and specifically relate to financial institutions.

In most cases, financial data sharing is quite limited to areas within financial services, but there are several benefits to customers and small businesses from open finance.

Increased Access to Financial Services: Data sharing allows customers to purchase and use financial services that previously they may not have had access to. For example, open financial data can support the credit assessment of borrowers by measuring utility, phone bills and other factors.

Enhanced User Convenience: Data sharing can save substantial time for customers in their engagement with financial services and, more importantly, for product purchases and exit. For example, open access to data on mortgage products enables customers to apply for loans without engaging a mortgage advisor.

Improved Product Options: Open financial data can provide an enhanced range of options available for customers and create further savings. For example, open data systems make it simpler to switch to different accounts, supporting small business customers to gain the best results.

Benefits of Open Data to Financial Institutions

Fintechs and other third-party services have displayed clear benefits by having the ability to access customer banking data that was previously unavailable in conventional banks. The benefits to other financial organisations aren’t necessarily as clear, but they do exist.

McKinsey explains that the open data systems are progressing in various ways that don’t necessarily translate into a clear win-lose situation for banks and fintech. Some banks will be able to leverage open banking and take a share of this emerging market. The McKinsey report several benefits from open banking for financial institutions:

Enhanced operational efficiency: open financial data could significantly reduce costs by replacing physical documents with verified digital data, making it simpler to adopt automated technologies. This will improve customer experience by enabling quicker and more transparent interactions.

Better Fraud Protection: Improved fraud protection can mean considerable cost reductions for financial businesses and an overall improved customer experience. Sharing fraud-related data creates more evidence and insight to support detecting any suspicious activity.

Improved Workforce Allocation: Financial organisations can use open data to allocate and support their workforce, assigning particular members to high-value activities.

Improve the Data Intermediation Process: Open banking systems create direct access to data via APIs for intermediation, reducing overall friction. Data sharing decreases or eliminates the costs financial organisations experience in data sourcing with third party providers and other aggregators.

July 21, 2021 by Mike Jones

Financial services businesses within the UK are becoming more reliant on scraping alternative data sources, with over 60% using alternative data to improve their decision-making process. The new report ‘The Growing Importance of Alternative Data in the Finance Industry’ by Oxylabs highlights the significant rise in web scraping for alternative data over the last year or so.

Over 200 senior data decision-makers in the UK finance industry were interviewed on their existing approach to data management. The findings indicated that web scraping and financial transitions were the most popular sources of alternative data for financial services organisations. This includes non-traditional data sources that may not have been assessed before, such as social media posts, website traffic and other data sources. Conventional sources like official public data and third-party data are still considered valuable but have been overtaken by the significant rise in alternative data.

Julius Cerniauskas, the CEO at Oxylabs, explains that the rise in online alternative data sources has created a sharp increase in demand for web scraping services from financial organisations looking to tackle the challenges from the pandemic.

Cerniauskas states that they have experienced a surge in inquiries from businesses in the financial services industry over the last year, so he explains that they are motivated to learn how these organisations were approaching data collection and analysis.

Alternative data can be implemented to gain a better understanding of business performance, market trends and future investment plans. Financial services businesses can transform alternative real-time data into clear, actionable insights that are far more likely to report significant improvements in decision-making.

Business leaders in the finance industry are continuing to explore new ways of improving investment decisions and reducing risk to their business, so it’s understandable to see that the global alternative data industry is growing and predicted to continue increasing over the next few years.

Looking at the research, it’s clear that financial services businesses are increasingly looking to utilise alternative data to gain more value and discover new insights into performance, industry trends and potential investment opportunities. Data-focused organisations will be in a stronger position to convert this valuable information into actionable insights and deliver strategic decisions in a post-pandemic economy.

July 21, 2021 by Mike Jones

Open banking is the transfer of financial data and a trend that inevitably is likely to increase. While open banking empowers fintech and larger technology businesses, a report by McKinsey suggests several significant benefits for established banks and financial organisations that may have not been previously recognised.

Industry experts highlight how our world is changing and how important information is to businesses. Customers are not willing to accept a particular service or price if they are aware of better options elsewhere. Several reports from McKinsey suggest that embracing open banking is necessary and has benefits for financial institutions as much as it had for fintech and other businesses. 

The findings indicate a value to the adoption of open financial data, an increase in GDP of between 1% and 1.5% within the U.S, the U.K. and the EU.

While it’s not exactly clear how open financial data will progress, the trend towards data sharing between financial institutions, fintech and other big businesses is only going to increase. This will have a significant impact on the traditional banking industry shortly.

In the report ‘Financial Services Unchained’, McKinsey explains that if open finance continues to accelerate it could transform the global financial services system and change the concept of banking altogether. The report goes onto say that the ability for customers to gain a deeper understanding of their finances could result in margin compression, as charges and pricing becomes more transparent. McKinsey explains that banks may have to compete with margin sharing, as payouts to other digital platforms could play a bigger role in customer acquisition.

McKinsey also highlights that open financial data places big technology businesses in a stronger position to become financial services leaders. We are increasingly seeing more big tech businesses entering financial services, using open data as part of their offerings. It’s worth remembering that multiple businesses are capable of using the same data and as a result, big technology businesses will have banking partners and will continue to face several banking competitors.

Increased competition will ultimately lead to the need to understand and respond to new changes, restructure offerings, adapt business models and establish partnerships with fintech or technology businesses to drive continued success and relevance.

The benefits and value of open data

While it may sound like conventional financial businesses may face a challenging future, the report ‘Financial Data Unbound’ by McKinsey details several benefits of open financial data and specifically relate to financial institutions.

In most cases, financial data sharing is quite limited to areas within financial services, but there are several benefits to customers and small businesses from open finance.

Increased Access to Financial Services: Data sharing allows customers to purchase and use financial services that previously they may not have had access to. For example, open financial data can support the credit assessment of borrowers by measuring utility, phone bills and other factors.

Enhanced User Convenience: Data sharing can save substantial time for customers in their engagement with financial services and, more importantly, for product purchases and exit. For example, open access to data on mortgage products enables customers to apply for loans without engaging a mortgage advisor.

Improved Product Options: Open financial data can provide an enhanced range of options available for customers and create further savings. For example, open data systems make it simpler to switch to different accounts, supporting small business customers to gain the best results.

Benefits of Open Data to Financial Institutions

Fintechs and other third-party services have displayed clear benefits by having the ability to access customer banking data that was previously unavailable in conventional banks. The benefits to other financial organisations aren’t necessarily as clear, but they do exist.

McKinsey explains that the open data systems are progressing in various ways that don’t necessarily translate into a clear win-lose situation for banks and fintech. Some banks will be able to leverage open banking and take a share of this emerging market. The McKinsey report several benefits from open banking for financial institutions:

Enhanced operational efficiency: open financial data could significantly reduce costs by replacing physical documents with verified digital data, making it simpler to adopt automated technologies. This will improve customer experience by enabling quicker and more transparent interactions.

Better Fraud Protection: Improved fraud protection can mean considerable cost reductions for financial businesses and an overall improved customer experience. Sharing fraud-related data creates more evidence and insight to support detecting any suspicious activity.

Improved Workforce Allocation: Financial organisations can use open data to allocate and support their workforce, assigning particular members to high-value activities.

Improve the Data Intermediation Process: Open banking systems create direct access to data via APIs for intermediation, reducing overall friction. Data sharing decreases or eliminates the costs financial organisations experience in data sourcing with third party providers and other aggregators.

July 16, 2021 by Mike Jones

Many business leaders believe that traditional practices can only deliver a certain level of results. This way of thinking has influenced financial and investment leaders to combine conventional data sources with other innovative practices. In the pursuit of delivering the highest results and gaining a market advantage in the finance industry, businesses are exploring new and obscure data sources that have actionable insights.

Alternative data refers to non-traditional data sources that finance and investment firms use to measure and guide their strategies. Examples of alternative data range from ESG information, credit card transaction to satellite imagery and weather data.
The number of alternative-data providers has grown to 20 times the size it was 30 years ago, with studies suggesting over 400 active providers on the market, compared to only 20 back in 1990.

Today, approximately half of all financial investment firms use alternative data and this number will likely continue growing as more businesses invest in new technology during and preceding the impacts of the pandemic. New data sources offer unique advantages and the opportunity to reveal new information that can differentiate a business from its competitors. Alternative data providers have increased considerably in recent years, but access to new data sources doesn’t necessarily mean an added advantage.

Comparing raw and aggregated data

Alternative data usually comes as aggregated data sets or as a straight data feed via APIs. Aggregated data is regarded as the more affordable option and is structured and easier to work with. However, these sets are more common, and because of that, they have less potential for alternative data structures. Alternative data specialists explain that this form often lacks depth, and businesses can lose the ability to explore their data in more unique ways.

The challenge today is how sure are we that a data set is creating value? It’s difficult to determine how much impact information is going to have until much later. Even with well-structured feeds and benchmarked data sets, the requirement for a skilled data analyst in the finance industry is only going to continue rising.

As discussed before, big data adoption is nothing new, and many businesses are currently utilising big data in some shape or form. However, alternative data has only really taken off in the last few years. Given the current level of adoption and the fact that over half of investment managers are now leveraging this form of data, some believe that alternative data could become more mainstream. Creating a data advantage, even it may be small can create a big difference in today’s competitive marketplace and support leaders with making quick and important decisions.

While hedge funds were one of the early adopters of alternative data, multiple industries now apply this form of data. Alternative data is openly available, and in many scenarios, it is free for all businesses. When executed and aggregated properly, alternative data creates powerful insights that were not possible in previous years.

The potential of alternative data is something more businesses are beginning to understand, and a growing number are taking advantage of this innovative data source. As it becomes more mainstream, it is likely to be adopted by more companies very soon.

Many business leaders believe that traditional practices can only deliver a certain level of results. This way of thinking has influenced financial and investment leaders to combine conventional data sources with other innovative practices. In the pursuit of delivering the highest results and gaining a market advantage in the finance industry, businesses are exploring new and obscure data sources that have actionable insights.

Alternative data refers to non-traditional data sources that finance and investment firms use to measure and guide their strategies. Examples of alternative data range from ESG information, credit card transaction to satellite imagery and weather data.
The number of alternative-data providers has grown to 20 times the size it was 30 years ago, with studies suggesting over 400 active providers on the market, compared to only 20 back in 1990.

Today, approximately half of all financial investment firms use alternative data and this number will likely continue growing as more businesses invest in new technology during and preceding the impacts of the pandemic. New data sources offer unique advantages and the opportunity to reveal new information that can differentiate a business from its competitors. Alternative data providers have increased considerably in recent years, but access to new data sources doesn’t necessarily mean an added advantage.

Comparing raw and aggregated data

Alternative data usually comes as aggregated data sets or as a straight data feed via APIs. Aggregated data is regarded as the more affordable option and is structured and easier to work with. However, these sets are more common, and because of that, they have less potential for alternative data structures. Alternative data specialists explain that this form often lacks depth, and businesses can lose the ability to explore their data in more unique ways.

The challenge today is how sure are we that a data set is creating value? It’s difficult to determine how much impact information is going to have until much later. Even with well-structured feeds and benchmarked data sets, the requirement for a skilled data analyst in the finance industry is only going to continue rising.

As discussed before, big data adoption is nothing new, and many businesses are currently utilising big data in some shape or form. However, alternative data has only really taken off in the last few years. Given the current level of adoption and the fact that over half of investment managers are now leveraging this form of data, some believe that alternative data could become more mainstream. Creating a data advantage, even it may be small can create a big difference in today’s competitive marketplace and support leaders with making quick and important decisions.

While hedge funds were one of the early adopters of alternative data, multiple industries now apply this form of data. Alternative data is openly available, and in many scenarios, it is free for all businesses. When executed and aggregated properly, alternative data creates powerful insights that were not possible in previous years.

The potential of alternative data is something more businesses are beginning to understand, and a growing number are taking advantage of this innovative data source. As it becomes more mainstream, it is likely to be adopted by more companies very soon.

There have been very few technological innovations that have impacted the finance industry, like Big Data. The traditional days of customers visiting local banks have been replaced with a wide range of diverse online products, as well as some use of in-branch services.

The banking industry experienced a significant transformation with the emerging digital changes. Abundant data sources are now available to financial businesses, enabling companies to gain a better understanding of their customers and create a more personalised service.
As more structured and unstructured data is generated by customers through loan applications, credit limits or online transactions, Big Data analytical tools are being utilised to create clear actionable insights. As an example, the Bank of America was one financial organisation that applied social media data to determine service issues with their customers that impact overall customer retention. When the bank used big data to assess thousands of comments on social media, they discovered lots of misinformation regarding purchase limits which potentially impacted their customer attraction and retention. Being capable of discovering customer issues quickly before they grow further is a powerful tool that big data technology can provide.

Businesses in the finance industry use several big data technologies such as artificial intelligence, machine learning and natural language processing. In a continuously increasing competitive market, businesses need to integrate innovative technology to gain a more competitive edge. A survey by Capgemini suggested that over 60% of financial businesses believe that Big Data analytics provides a significant competitive advantage and over 90% believe that successful big data measures will determine the leaders of the future.

The restrictions implemented from the pandemic have placed more emphasis on the digital services offered and available to their customers. While the transition to digital is nothing new, the impacts of the last year have accelerated this movement to new and innovative services. As physical branches reduced their hours or temporarily closed, many financial services have moved online. Without the support of big data tools, banks have become overwhelmed by the high volume of new applications and enquiries. Customers that experience delays or waiting times could potentially move to an alternative bank that offers better customer service.

Banks need to remain focused on assessing all factors before offering credit to a customer or approving a loan. Using relevant customer data with big data technologies improves this process and enhances overall risk management. The more data credit risk management solutions available, the more accurate the credit scoring will be.

The transition and rise of digital have brought a higher incidence of fraud as many face-to-face transactions have been replaced with online services. HSBC uses machine learning and AI to explore potential fraud in various ways by checking IP addresses and monitoring irregular transactions. But customer service remains the top priority for deploying big data technologies. During the pandemic, the bank experienced a significant rise in customer enquiries, and chatbots became vital communication tools. Using Natural Language Processing technology, chatbots can convert text and connect it to established patterns to deliver relevant answers. The text is fed through machine learning tools to determine concerns or challenges faced by their customers.

Standard Chartered Bank uses big data to gain more insights into customer behaviour and target them with specialised services and deals. With real-time data and analytics, valuable information is generated from regular transactions.

As the Economist declared a few years ago, the world’s most valuable resource is no longer oil but data. There is a definitive need for financial businesses to embrace the benefits of big data moving forward. The global market for big data analytics is forecast to increase by an annual rate of over 22% until 2026. Financial businesses are more aware of the necessity of integrated big data tools into certain areas of their business. Big data tools are continuing to influence the financial landscape and support customers issues, increase retention rates and reveal specific insights about customer behaviour.

There have been very few technological innovations that have impacted the finance industry, like Big Data. The traditional days of customers visiting local banks have been replaced with a wide range of diverse online products, as well as some use of in-branch services.

The banking industry experienced a significant transformation with the emerging digital changes. Abundant data sources are now available to financial businesses, enabling companies to gain a better understanding of their customers and create a more personalised service.
As more structured and unstructured data is generated by customers through loan applications, credit limits or online transactions, Big Data analytical tools are being utilised to create clear actionable insights. As an example, the Bank of America was one financial organisation that applied social media data to determine service issues with their customers that impact overall customer retention. When the bank used big data to assess thousands of comments on social media, they discovered lots of misinformation regarding purchase limits which potentially impacted their customer attraction and retention. Being capable of discovering customer issues quickly before they grow further is a powerful tool that big data technology can provide.

Businesses in the finance industry use several big data technologies such as artificial intelligence, machine learning and natural language processing. In a continuously increasing competitive market, businesses need to integrate innovative technology to gain a more competitive edge. A survey by Capgemini suggested that over 60% of financial businesses believe that Big Data analytics provides a significant competitive advantage and over 90% believe that successful big data measures will determine the leaders of the future.

The restrictions implemented from the pandemic have placed more emphasis on the digital services offered and available to their customers. While the transition to digital is nothing new, the impacts of the last year have accelerated this movement to new and innovative services. As physical branches reduced their hours or temporarily closed, many financial services have moved online. Without the support of big data tools, banks have become overwhelmed by the high volume of new applications and enquiries. Customers that experience delays or waiting times could potentially move to an alternative bank that offers better customer service.

Banks need to remain focused on assessing all factors before offering credit to a customer or approving a loan. Using relevant customer data with big data technologies improves this process and enhances overall risk management. The more data credit risk management solutions available, the more accurate the credit scoring will be.

The transition and rise of digital have brought a higher incidence of fraud as many face-to-face transactions have been replaced with online services. HSBC uses machine learning and AI to explore potential fraud in various ways by checking IP addresses and monitoring irregular transactions. But customer service remains the top priority for deploying big data technologies. During the pandemic, the bank experienced a significant rise in customer enquiries, and chatbots became vital communication tools. Using Natural Language Processing technology, chatbots can convert text and connect it to established patterns to deliver relevant answers. The text is fed through machine learning tools to determine concerns or challenges faced by their customers.

Standard Chartered Bank uses big data to gain more insights into customer behaviour and target them with specialised services and deals. With real-time data and analytics, valuable information is generated from regular transactions.

As the Economist declared a few years ago, the world’s most valuable resource is no longer oil but data. There is a definitive need for financial businesses to embrace the benefits of big data moving forward. The global market for big data analytics is forecast to increase by an annual rate of over 22% until 2026. Financial businesses are more aware of the necessity of integrated big data tools into certain areas of their business. Big data tools are continuing to influence the financial landscape and support customers issues, increase retention rates and reveal specific insights about customer behaviour.