Automation in Financial Services to Enhance Data Capabilities
Data has become a critical component of business strategy and operations, with its value steadily increasing over time, making storage resources challenging to handle. The International Data Center (IDC) predicts the global data sphere will reach 163 zettabytes by 2025. That amounts to 163 trillion gigabytes or ten times the amount recorded in 2016.
The time has never been better to take the next step in digital acceleration, as technology is undergoing a revolutionary transformation in the financial industry.
| According to International Data Corporation (IDC), banking will be one of the two industries spending the most on AI solutions by 2024. | |
| According to Gartner’s RPA Stats, approximately 80% of finance leaders have implemented or plan to implement Robotic Process Automation (RPA). |
Automation in the banking industry can extend well beyond automation processes, impacting the entire organisation to enhance compliance, security, and relationships with customers and the workforce. In this post, we’ll explain the following:
Section 1: What is Automation?
Section 2: What are the different types of automation?
Section 3: Why do financial services need automation?
Section 4: How is automation used in the financial industry?
Section 5: What are the benefits of automation?
Section 6: How to start with Data Automation
Section 7: What are the automation strategies in the financial sector?
Section 8: What is the future of automation?
Section 9: Start to automate & integrate with Finworks Data Fabric
What is Automation?
Automation refers to leveraging technology to perform tasks with minimal or no human intervention. This does not imply replacing humans with robots but instead using automation to handle monotonous and time-consuming manual tasks.
By automating such activities in the financial sector, finance departments can focus on creating value and driving strategy. The main objective of automation is to enhance process efficiency by reducing or eliminating repetitive tasks or activities that do not add value. Automation also plays a crucial role in achieving business process excellence.
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Basic automation:
Basic automation involves automating simple and fundamental tasks. It aims to digitise work by using tools to streamline and centralise routine tasks. For instance, a data management platform can replace disconnected silos of information. Business process management (BPM) and robotic process automation (RPA) are two examples of basic automation.
Process automation:
Process automation, on the other hand, focuses on managing business processes to ensure consistency and transparency. By implementing process automation, a business can improve productivity and efficiency. Moreover, it can provide new insights into business challenges and suggest solutions. Workflow automation and process mining are two types of process automation.
Workflow automation is the use of technology to automate the manual steps involved in a business process or workflow. The goal is to reduce the time, effort, and errors associated with manual processes while improving efficiency and productivity.
Workflow process automation involves breaking down a complex business process into smaller, simpler steps and automating each step using software tools and techniques. These steps can include data entry, document routing and approval, notifications, reminders, and more.
There are several benefits of workflow process automation, including:
| Increased efficiency: Workflow process automation can eliminate manual tasks and reduce the time required to complete a process. This allows employees to focus on more important tasks and reduces the risk of errors. | |
| Improved productivity: Automation can streamline processes and improve productivity by allowing employees to complete tasks faster and with fewer errors. | |
| Better collaboration: Workflow process automation can improve collaboration between teams by providing a centralised platform for sharing information and tracking progress. | |
| Reduced costs: By eliminating manual tasks and reducing errors, workflow process automation can help organisations reduce costs associated with manual labour and rework. | |
| Enhanced customer experience: Workflow process automation can improve the customer experience by enabling faster response times and reducing errors and delays. |
Overall, workflow process automation can help organisations streamline their business processes, improve efficiency and productivity, and reduce costs while enhancing the customer experience.
Data Management Automation:
Data management automation refers to the use of technology to automate the process of managing and maintaining data. It involves using software tools and techniques to streamline data management tasks such as data storage, data processing, data analysis, and data retrieval.
The goal of data management automation is to reduce the manual effort required to manage data, minimise errors, and improve the efficiency of data management processes. There are several approaches to data management automation, including:
| Data integration and transformation: This involves automating the process of integrating and transforming data from various sources into a single, unified format. | |
| Data validation and quality control: This involves automating the process of validating data and ensuring that it meets certain quality standards. | |
| Data backup and recovery: This involves automating the process of backing up data and restoring it in case of a system failure or data loss. | |
| Data security: This involves automating the process of securing data, including data encryption and access control. | |
| Data governance: This involves automating the process of managing data policies and procedures, including data privacy and compliance. |
Overall, data management automation enables organisations to reduce the time and effort required to manage data while also improving the accuracy and reliability of data management processes.
Artificial intelligence (AI) automation:
Automation based on artificial intelligence (AI) is the most advanced type. AI allows machines to “learn" from their experiences and make decisions based on that knowledge. AI automation involves a process of training machine learning models using large volumes of data to recognise patterns, make predictions, and automate decision-making. This technology can be applied to a variety of tasks, including data entry and processing, customer service, quality control, and more.
Basic automation:
Basic automation involves automating simple and fundamental tasks. It aims to digitise work by using tools to streamline and centralise routine tasks. For instance, a data management platform can replace disconnected silos of information. Business process management (BPM) and robotic process automation (RPA) are two examples of basic automation.
Process automation:
Process automation, on the other hand, focuses on managing business processes to ensure consistency and transparency. By implementing process automation, a business can improve productivity and efficiency. Moreover, it can provide new insights into business challenges and suggest solutions. Workflow automation and process mining are two types of process automation.
Workflow automation is the use of technology to automate the manual steps involved in a business process or workflow. The goal is to reduce the time, effort, and errors associated with manual processes while improving efficiency and productivity.
Workflow process automation involves breaking down a complex business process into smaller, simpler steps and automating each step using software tools and techniques. These steps can include data entry, document routing and approval, notifications, reminders, and more.
There are several benefits of workflow process automation, including:
| Increased efficiency: Workflow process automation can eliminate manual tasks and reduce the time required to complete a process. This allows employees to focus on more important tasks and reduces the risk of errors. | |
| Improved productivity: Automation can streamline processes and improve productivity by allowing employees to complete tasks faster and with fewer errors. | |
| Better collaboration: Workflow process automation can improve collaboration between teams by providing a centralised platform for sharing information and tracking progress. | |
| Reduced costs: By eliminating manual tasks and reducing errors, workflow process automation can help organisations reduce costs associated with manual labour and rework. | |
| Enhanced customer experience: Workflow process automation can improve the customer experience by enabling faster response times and reducing errors and delays. |
Overall, workflow process automation can help organisations streamline their business processes, improve efficiency and productivity, and reduce costs while enhancing the customer experience.
Data Management Automation:
Data management automation refers to the use of technology to automate the process of managing and maintaining data. It involves using software tools and techniques to streamline data management tasks such as data storage, data processing, data analysis, and data retrieval.
The goal of data management automation is to reduce the manual effort required to manage data, minimise errors, and improve the efficiency of data management processes. There are several approaches to data management automation, including:
| Data integration and transformation: This involves automating the process of integrating and transforming data from various sources into a single, unified format. | |
| Data validation and quality control: This involves automating the process of validating data and ensuring that it meets certain quality standards. | |
| Data backup and recovery: This involves automating the process of backing up data and restoring it in case of a system failure or data loss. | |
| Data security: This involves automating the process of securing data, including data encryption and access control. | |
| Data governance: This involves automating the process of managing data policies and procedures, including data privacy and compliance. |
Overall, data management automation enables organisations to reduce the time and effort required to manage data while also improving the accuracy and reliability of data management processes.
Artificial intelligence (AI) automation:
Automation based on artificial intelligence (AI) is the most advanced type. AI allows machines to “learn" from their experiences and make decisions based on that knowledge. AI automation involves a process of training machine learning models using large volumes of data to recognise patterns, make predictions, and automate decision-making. This technology can be applied to a variety of tasks, including data entry and processing, customer service, quality control, and more.
With the help of automation, financial institutions can streamline their processes by integrating disparate systems. This reduces the load on employees, making them productive and more effective in their jobs because they have the resources (information and time) to give customers the best service possible.
| Financial industry processes can be complex and varied, making it challenging to reconcile accounts. Automation can review and reconcile data, requiring minimal human intervention. | |
| Automation can help with compliance by ensuring expense records comply with regulations and communicating policy infractions and data discrepancies to the appropriate parties. Automating the fraud detection process can help financial services companies detect fraudulent transactions in real-time, reducing the risk of financial losses. | |
| Keeping daily records of business transactions, profit, and loss can help banks plan and detect problems early. Automation can improve and fix existing business techniques and processes. | |
| Banks must provide substantial reports that show performance, statistics, and trends using large amounts of data. Automation can collect and analyse data from multiple sources and formats, making forecasting and planning easier. | |
| Automating financial reporting processes can help financial services companies ensure compliance with regulatory requirements and improve the accuracy and timeliness of financial reporting. | |
| Financial services companies use data analysis to inform business decisions and identify opportunities for growth. Automating the process of data analysis can help companies identify patterns and trends in their data and make better-informed decisions. |
Section 4:
How is automation used in the financial industry?
Some examples of commonly used cases for banking automation are:
Customer onboarding:
By automating the customer onboarding process, we can ensure accuracy and compliance, save time, and streamline the collection and verification of customer data. Automating the customer onboarding process can help financial institutions improve the customer experience by enabling faster account opening, reducing the need for manual data entry, and ensuring compliance with regulatory requirements.
Loan origination/credit processing:
Automation bridges the gap between siloed databases, providing visibility of all the information required for status reporting and making data-driven decisions that expedite the resolution of cases.
Regulatory Compliance:
Financial services companies must comply with a range of regulatory requirements, including reporting requirements. Automating the process of compliance reporting can help companies ensure that they are meeting regulatory requirements and avoid costly fines and penalties. When highly regulated processes are automated, compliance can be built into the processes and tracked in a single platform, resulting in increased visibility and immediate access to an audit trail for streamlined reporting.
Fraud prevention:
By centralising data on one platform, banks gain a more holistic view of their clientele, and financial dealings make it simpler to spot suspicious activity. Financial services companies must ensure that their data is accurate and up to date. Automating the process of data cleansing can help companies detect and correct errors and inconsistencies in their data.
Back-office operations:
Digitising tasks and workflows in the back-office increases productivity, decreases errors and saves cost by eliminating wasted time spent searching for and filing documents. Financial services companies must store large volumes of data, often for long periods of time. Automating the process of data storage can help companies ensure that their data is secure and easily accessible when needed.
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By implementing automation in certain processes, financial services can save time and cost while improving efficiency. Accenture estimates that as much as 80% of financial operations could be automated, relieving financial experts of 60%-75% of their time on mundane tasks.
There are several advantages to using automation in the financial industry:
| Timesaving : | Manual processes like account reconciliation and variance analysis can be tedious and time-consuming. Modern accounting systems have eliminated the need for manual processes resulting in timesaving. |
| Cost-saving : | Manually collecting, preparing, transforming, and analysing data can be a waste of resources and isn’t cost-effective. Automation can perform these tasks more efficiently and effectively at a lower cost. |
| Reduced errors : | By automating data collection, businesses can gain visibility into their entire financial pipelines, including contracts, invoices, and vendor information, without having to switch between different programmes or manually sort the data manually. |
| Better manage risk : | Finance executives can run scenarios with different variables (such as interest rate, inflation, or currency fluctuations). Automating this kind of data assesses potential risks in existing markets and opportunities in new ones and access accurate and timely information from across the organisation. |
| Improved decision-making : | Data-driven decision-making is highly valued in the business world. Would you prefer to make decisions based on manual data entry and reporting or based on precise and accurate data that reflects the reality of your business? |
By implementing automation in certain processes, financial services can save time and cost while improving efficiency. Accenture estimates that as much as 80% of financial operations could be automated, relieving financial experts of 60%-75% of their time on mundane tasks.
There are several advantages to using automation in the financial industry:
Timesaving
Manual processes like account reconciliation and variance analysis can be tedious and time-consuming. Modern accounting systems have eliminated the need for manual processes resulting in timesaving.
Cost-saving
Manually collecting, preparing, transforming, and analysing data can be a waste of resources and isn’t cost-effective. Automation can perform these tasks more efficiently and effectively at a lower cost.
Reduced errors
By automating data collection, businesses can gain visibility into their entire financial pipelines, including contracts, invoices, and vendor information, without having to switch between different programmes or manually sort the data manually.
Better manage risk
Finance executives can run scenarios with different variables (such as interest rate, inflation, or currency fluctuations). Automating this kind of data assesses potential risks in existing markets and opportunities in new ones and access accurate and timely information from across the organisation.
Improved decision-making
Data-driven decision-making is highly valued in the business world. Would you prefer to make decisions based on manual data entry and reporting or based on precise and accurate data that reflects the reality of your business?
If you consider any other type of data to be crucial for your enterprise activities, you should include it in the automation pipeline. This reduces your reliance on resources and makes maintaining data integrity and quality easier over time.
Here’s a list to help you decide which applications are best for data automation:
| Is it necessary to update the data regularly? | |
| Is it necessary to manipulate it before uploading/processing? | |
| Is there a tremendous amount of data? | |
| Is the data coming from a variety of sources? |
Section 7:
What are the Automation Strategies in the Financial Sector?
Data quality automation
A unified view of the customer is essential, but many businesses have difficulty centralising and updating their master data. The use of an automated programmatic layer, which can be an effective tool in maintaining data quality, is being increasingly adopted by financial services companies to aggregate data and provide a holistic customer view across data sources.
Robotic process automation (RPA)
RPA is a powerful tool for cutting operational expenses while boosting performance and accuracy. By minimising or eliminating the need for human intervention, RPA can boost efficiency and accuracy in all areas of a bank’s operations, from the front to the middle to the back.
Intelligent data automation
Intelligent data can be used to achieve better and faster results. Its use in financial reporting allows for data verification and reporting improvement by extracting critical data and reviewing legal documents. This type of automation will fill data and regulatory gaps without manual intervention.
Workflow automation
Integration of document analysis, behaviour, and pattern data from various sources enables automated document, report, audit trail, and notification creation via workflow automation. Tasks that may otherwise sit in people’s inboxes for extended periods, causing delays in the process due to inaction, can now be assigned automatically.
Link analysis automation
Analysing the connections between different pieces of information is a powerful tool for discovery, analysis, and review. A comprehensive picture emerges by analysing the connections between customers and their internal and external accounts to the company.
Automation is one of the innovative solutions that has matured over the last few years. They are making a much more desirable and viable option for banks and financial institutions to reduce costs and improve accuracy in response to the growing demand for cheaper, more streamlined, and more accurate automated services.
Among the most valuable forms of automation for banks and other financial institutions are the following:
| 1. Data quality automation | A unified view of the customer is essential, but many businesses have difficulty centralising and updating their master data. The use of an automated programmatic layer, which can be an effective tool in maintaining data quality, is being increasingly adopted by financial services companies to aggregate data and provide a holistic customer view across data sources. |
| 2. Robotic process automation (RPA) | RPA is a powerful tool for cutting operational expenses while boosting performance and accuracy. By minimising or eliminating the need for human intervention, RPA can boost efficiency and accuracy in all areas of a bank’s operations, from the front to the middle to the back. |
| 3. Intelligent data automation | Intelligent data can be used to achieve better and faster results. Its use in financial reporting allows for data verification and reporting improvement by extracting critical data and reviewing legal documents. This type of automation will fill data and regulatory gaps without manual intervention. |
| 4. Workflow automation | Integration of document analysis, behaviour, and pattern data from various sources enables automated document, report, audit trail, and notification creation via workflow automation. Tasks that may otherwise sit in people’s inboxes for extended periods, causing delays in the process due to inaction, can now be assigned automatically. |
| 5. Link analysis automation | Analysing the connections between different pieces of information is a powerful tool for discovery, analysis, and review. A comprehensive picture emerges by analysing the connections between customers and their internal and external accounts to the company. |
Incorporating this data into customer segmentation and scoring models helps identify customer links with bad actors, dubious jurisdictions, criminal histories, and companies and analyse the ultimate beneficiary ownership.
No-code will redefine application development
When compared to traditional computer programming, no-code development platforms (NCDPs) and their close relatively low-code platforms make it possible for programmers and non-technical users to create applications through the use of graphical user interfaces and configurations. Even though the platforms are still in their infancy, they have the potential to lessen the demand for expensive and in-demand software specialists.
Technically speaking, NCDP is the integration and application of software engineering practices like component reuse and assembly, domain-specific languages, fast visual development tools, programmable workflow process orchestration, and design thinking. Progress in NCDP is intrinsically linked to the evolution of cloud computing, DevOps, and other technologies that address issues like containerisation, inflexible scaling, and the upkeep of highly available computing environments.
Open source, SaaS, and serverless reduce barriers to entry
Technology companies and traditional financial institutions launching new fintech businesses require open-source software, serverless architecture, and software as a service (SaaS).
SaaS enables businesses to utilise the software on demand without investing in and maintaining it, while serverless architecture frees businesses from maintaining their own servers because fees are only incurred when software is run, rather than being generated continuously regardless of business need. It promotes flexible scaling that prevents idling and loss, which boosts productivity during development. Since open-source software provides developers with access to useful, freely distributed source code, it is a huge benefit for fast-growing businesses that want to expand their operations.
Cloud computing will liberate financial services
By removing the need for costly investments in IT infrastructure and data centres, cloud computing frees financial institutions to focus on their core competencies while providing them with affordable, scalable IT resources. However, the cloud is also giving rise to new formats like open banking and banking-as-a-service, which are reshaping the traditional relationship between customers and financial service providers.
The incorporation of more nimble capabilities and the introduction of brand-new businesses that demand rapid response to market and customer needs, as well as scalable, flexible infrastructure, will keep financial institutions firmly embedded in the cloud. Meanwhile, the widespread use of big data analytics will drive up interest in cloud-based elastic computing, which can adapt its computing resources on the fly to meet fluctuations in demand.
Over the next decade, certain technologies will shape the financial industry's competitive landscape. To meet the automation and integration requirements, you must choose automation technology that will allow you to deliver faster and more seamless services.
No-code will redefine application development
When compared to traditional computer programming, no-code development platforms (NCDPs) and their close relatively low-code platforms make it possible for programmers and non-technical users to create applications through the use of graphical user interfaces and configurations. Even though the platforms are still in their infancy, they have the potential to lessen the demand for expensive and in-demand software specialists.
Technically speaking, NCDP is the integration and application of software engineering practices like component reuse and assembly, domain-specific languages, fast visual development tools, programmable workflow process orchestration, and design thinking. Progress in NCDP is intrinsically linked to the evolution of cloud computing, DevOps, and other technologies that address issues like containerisation, inflexible scaling, and the upkeep of highly available computing environments.
Open source, SaaS, and serverless reduce barriers to entry
Technology companies and traditional financial institutions launching new fintech businesses require open-source software, serverless architecture, and software as a service (SaaS).
SaaS enables businesses to utilise the software on demand without investing in and maintaining it, while serverless architecture frees businesses from maintaining their own servers because fees are only incurred when software is run, rather than being generated continuously regardless of business need. It promotes flexible scaling that prevents idling and loss, which boosts productivity during development. Since open-source software provides developers with access to useful, freely distributed source code, it is a huge benefit for fast-growing businesses that want to expand their operations.
Cloud computing will liberate financial services
By removing the need for costly investments in IT infrastructure and data centres, cloud computing frees financial institutions to focus on their core competencies while providing them with affordable, scalable IT resources. However, the cloud is also giving rise to new formats like open banking and banking-as-a-service, which are reshaping the traditional relationship between customers and financial service providers.
The incorporation of more nimble capabilities and the introduction of brand-new businesses that demand rapid response to market and customer needs, as well as scalable, flexible infrastructure, will keep financial institutions firmly embedded in the cloud. Meanwhile, the widespread use of big data analytics will drive up interest in cloud-based elastic computing, which can adapt its computing resources on the fly to meet fluctuations in demand.
It is now time for your organisation to recognise the value of enterprise data. Starting with a data strategy will establish a foundation for new and exciting ways for your data to deliver benefits. Data fabric enables faster extraction of insights, and embedded governance helps in data security, which is important for a highly regulated industry like financial services.
Finworks Data Fabrics can ultimately be the critical building block to a global data integration and management strategy enabling your financial services data to shine as a valuable asset in this data-driven era. Data fabric enables faster extraction of insights, and embedded governance helps in data security, which is important for a highly regulated industry like financial services.
The Finworks Workflow management platform revolutionises collaboration within financial services by making processes transparent in real time, showing the current workload and highlighting bottlenecks. The platform has a low-code user interface that enables enterprise-grade automation of workflows and case management. The secure structure and efficient automation ensure compliance and result in measurable time savings.