What’s the Right Approach to Process Automation for Fintech?

Choosing the right approach to process automation for Fintech means that organizations need to consider the core aims and unique business requirements of this disruptive sector.

Novel fintech like cryptocurrency and blockchain have disrupted and fundamentally reimagined such previously eminent and staid realities such as the government monopoly on currency-creation. As fintech has risen to prominence, an unlikely mascot has emerged to represent the convenience enabled by novel financial technologies; mobile banking ads have portrayed ballerinas paying for goods while performing- logging into Visa or Chase apps from the wings, or hilariously, paying for goods while doing a “grand jete between two grand pianos,” in one tongue-in-cheek commercial by Chase. They portray a shared sentiment: that we are too locked in to our daily commitments to go to the bank. Fintechs promise convenience, mobility, and speed of service in a traditional sector. But in a novel industry where security and accountability are of the essence, how technology-forward can fintechs be internally, with adopting cloud-native integration and business process automation? The process automation strategies that fintechs adopt are a key differentiator, in terms of their CX and service model, from that of traditional banks.

So what’s the right approach to process automation for fintech? Before we explore the answer to that question, let’s review what fintech automation is.

Related: The future of finance automation

What is Fintech Automation?

It’s when a fintech organization uses automation to streamline their processes end-to-end. This requires the fintech company to use an enterprise automation platform that can “listen” to their apps for business events (triggers), where once the conditions get met, the platform can deliver real-time outcomes (actions).

Fintechs Have Different Integration and Enterprise Automation Requirements than Traditional Banks. 

Back in the day, founding a financial institution was a process that involved political jostling and possibly even dueling for your honor. Young fintech companies were born into a different world. For example, for fintechs, it’s a cloud-native era, that moves faster than the world traditional banks were created in. They are often working with a startup business model and rate of growth, where time and value are of the essence, toward the goal of creating transformative disruption in a traditional sector. 

Enterprise automation enables the transformative change that fintech promises to instigate. Automation makes it possible for companies to deliver on the reduction of internal costs and speed of service that fintechs offer compared to traditional banks. However, because they’re smaller, leaner operations than august institutions like J.P. Morgan or Goldman Sachs, fintechs also have different IT requirements for integration and automation. For example, an on-premise deployment of a traditional integration platform involving server provisioning makes less sense for a fintech than for a bank. CB Insights notes that Fintech is being disrupted by technology from the back-office (risk/compliance), middle-office (business intelligence), and front-office (hedge fund tech, portfolio management). With a growing tech landscape, integration and automation will likely become increasingly important. So, what’s the right approach to automation- which approach can meet modern companies’ specific IT requirements and enable fintech companies to achieve their vision?

As fintech companies evaluate integration and automation platforms to manage their SaaS applications, databases, and microservices, they should look for the following criteria:

  • Agile platform that facilitates rapid time-to-deployment
  • Adapts to changing conditions
  • Unified, elegant solution to both integration and automation
  • Cloud-based, serverless enterprise automation
  • Alignment of business goals and automation strategy
  • Real-time, event-driven data operations
  • Orchestrating internal processes across SaaS apps

Related: The top differences between an iPaaS and an ETL tool

Understanding Different Automation Strategies

There’s a lot of confusion in the market about automation, and companies are unclear on which is the tool I can use for everything. RPA? iPaaS? Bots? What is a cohesive strategy for automation in your company?


RPA is a UI-based process automation tool. RPA is not a comprehensive automation solution, but it is still relevant for some tasks. RPA performs some very specific tasks well, like extracting information from a legacy system on a mainframe. RPA works best in conditions where the UI isn’t going to change. You can connect to an RPA’s API to incorporate it into automated workflows, using cloud-based enterprise automation. 


iPaaS is an integration platform offered on a subscription basis. iPaaS is not necessarily cloud-based; in fact, many require server deployment (referred to as ‘atoms’ or ‘nodes’). iPaaS is generally operated by an organization’s IT department.


Bots are software that performs automated tasks by running a script. They can be used to orchestrate work in messaging platforms like Slack. Bots can respond to requests, to facilitate the completion of processes, work, or interactions. 


iSaaS is a term used for integration software offered on a subscription basis, that doesn’t offer the security features or robust integration capabilities of iPaaS. iSaaS is often used by individual employees to automate tasks like directing incoming emails to a Slack channel or spreadsheet.

Enterprise Automation

Enterprise automation is cloud-native and uses API connections to offer enterprise application integration, data automation, and workflow automation between SaaS apps, databases and microservices. Enterprise automation couples robust integration capabilities with automation. Enterprise automation also makes it possible for users outside of the IT department to work collaboratively with IT in designing automations for their function or department, because the UI is user-friendly and automations designed in the platform are centrally governable. 

Related: A closer look at the meaning of process orchestration

Real Time vs. Batch Processes

Real-time automations operate at the pace at which business events, such as sales calls or IT provisioning, occur. Batch processes occur on a schedule, for example, traditional finance departments may have a nightly ETL sync that moves all the customer records from the day from one application to the data warehouse. Automated data syncs that occur in real-time offer business value, because they ensure that the single source of truth for data in the organization is consistently accurate throughout the day or week. Additionally, automated business processes and workflow automations rely on real-time operations. 

Cloud-Based Enterprise Automation Breaks Down Dichotomies Between iPaaS and Process Automation

CIOs or Business Systems leads looking for an automation solution may feel boxed in, as they’re presented with dichotomies between robustness and ease-of-use, or with tools whose limited range of functionality will leave them with a fragmented array of automation tools that only meet individual facets of the integration and automation needs across their enterprise. However, cloud-based enterprise automation breaks down perceived dichotomies between the integration power of iPaaS and the workflow automation capabilities offered by automation-specific tools. 

Expansive Scope

Enterprise automation was designed to support all of the core use-cases for digital transformation, with best-of-breed capabilities. It offers robust integration with user-friendly automation, so that businesses can have one unified, elegant solution for all use-cases.


Enterprise automation also offers extensibility. You can build custom connectors for on-premise or cloud applications not yet supported by the platform. You can connect to printers and scanners, SAP, databases- developers don’t feel boxed-in. Within automated processes and workflows, you can write code for data transformations in individual steps of the recipes. There are approximately 400 formulas included, like in Excel, and you can extend the existing formulas by writing your own code. 

How Automation Strategy is a Differentiator in Fintech Processes

The value of automation for fintech:

  • Reduce fraud
  • Reduce internal operating costs
  • Increase internal operational efficiency
    • Reduce human error
    • Faster loan and credit approval processes
    • Process data in real-time 

Fraud Reduction for Fintech

High transaction volumes are profitable. Digital payment enablement companies like Stripe and Paypal make their revenue from transaction fees. So when addressing the issue of fraud reduction, you need to address it without sacrificing the quality of customer experience or ease of transactions. But money is lost on fraud- when the merchant doesn’t follow through with providing the goods, or the digital payment enablement platform has to pay out to cover losses for the customers. Cutting down on these issues raises margins, and huge advances in this process have been made in the last 10-15 years, with ML for pattern-recognition and automated identity verification processes across multiple devices.  

Traditionally, organizations would address fraud by, for example, declining transactions or shutting down a card if a transaction seemed out of the ordinary. This creates a lot of hassle for customers. However, new AI and ML tools make fraud detection easier. Additionally, automated identity verification processes, such as triggering an SMS message to a user’s cell phone for two-step authentication, is done instantly thanks to automated processes. That way customers can continue their customer journey without frustrations like declined credit cards or protracted identity verification processes.

According to research by analytics leader SAS and the Association of Certified Fraud Examiners (ACFE), “While only 13% of organizations use artificial intelligence (AI) and machine learning to detect and deter fraud, another 25% plan to adopt such technologies in the next year or two – a nearly 200% increase.” Deloitte notes that when organizations use machine learning, “these systems can also scale up to meet the demands of big data with greater flexibility than traditional methods used for fraud prevention and detection.”

Deloitte reports that companies will find increased value with machine learning for automated fraud detection, noting that it offers improved capabilities over traditional RPA techniques used for fraud detection: “RPA techniques tend to work efficiently primarily in a structured data environment. In today’s day and age, however, the amount of data being produced and the complexity of analysis has grown to unprecedented levels.” 

Machine learning tools offered as SaaS services, like Tensorflow, can be integrated into an organization’s tech stack and incorporated into automated workflows with an enterprise automation platform and used for fraud detection or detecting invoice inaccuracies.

Credit Line Approval

Fundbox, a B2B payments and credit network designed to accelerate B2B commerce by providing businesses with loans and credit lines, uses automation to sync customer data in real-time from Fundbox’s Snowflake data warehouse into their Salesforce CRM. This expedites the lending process and enables better customer service, because the sales team and client services team are able to quickly follow up on customer credit applications based on the accurate and immediate data in Salesforce. Sales team needs to be able to quickly respond with accurate data, particularly in situations in which a customer is applying for credit but fails to complete the application process. This data flow automation enables teams to quickly provide personalized service and loan decisions.

Faster Loan Approval Processes with Automation

The Economist reports that tech companies are reviving point-of-sale consumer lending, and that “In the first quarter of 2018, personal-loan balances in America surged by 18% year-on-year to $120bn.” It’s also a mature and growing process for fintech: according to Forbes, “online lending is a more mature area of the fintech market, but that doesn’t mean it will see a slowdown next year.” 

Automating manual loan approval processes, using an enterprise automation platform that can incorporate AI and ML capabilities into the automation, makes it possible to handle point-of-sale loans quickly (in a matter of seconds), and incorporate the loan process more seamlessly into consumer transactions. 

Compare this to traditional loan approval processes; for a traditional credit card application, you fill out page after page of paperwork, pencilling in your annual income and address into small square boxes. You mail the application out (or submit it online). After this process, you will wait for days, or weeks, to receive a letter in the  mail to find out if you’ve even been approved. Then, on the off chance that you don’t mistake the letter for junk mail and throw it away, you’ll either have a credit card or a rejection letter. 

Apple exemplifies this rapid credit approval. You can get an Apple credit card in about three minutes. Three minutes, compared to several weeks. That’s a fundamentally different customer experience. How does this happen? With automation. The metrics that define the approval process are concretized; the automation checks FICO data and the yes / no is clear. Automation for the fintech and digital payment enablement sector isn’t just about streamlining internal processes like employee onboarding or syncing data between applications- it shapes the customer experience and plays a big role in which companies customers are going to turn to for their credit needs.

Meet fintech industry-specific regulations and connect proprietary applications

Since fintech companies often face industry-specific regulations and may be using proprietary applications, an out-of-the-box integration tool is likely not going to meet their specific needs.

Mosaic, a fintech company that offers financing for solar installation, uses automation to move data from their CRM to their proprietary internal application. Due to industry regulations, their external dealer sales reps are required to take a compliance quiz. Mosaic has the reps take the quiz on their customer relationship management (CRM) platform, and then they use automation to pull the data into their internal, proprietary loan platform. 

Mosaic also uses automation in their support workflow to move support ticket data securely from Zendesk to their CRM, which was created using Quick Base. Now, instead of downloading reports and uploading them into a different application manually, they use automation to reduce data silos at their organization.  

Reduce internal operating costs with process automation

Reducing internal operating costs and scaling processes in a cost-effective way is crucial to fulfilling fintech’s promise of a more consumer-friendly approach to banking. The Economist notes that “if the world’s listed banks chopped expenses by a third, the saving would be worth $80 a year for every person on Earth,” and that if fintech can successfully reimagine financial services, “the system will get better at its vital job of allocating capital.”

Reducing internal operating costs and scaling processes in a cost-effective way is crucial to fulfilling fintech’s promise of a more consumer-friendly approach to banking. Share on X

To cut internal costs, fintech companies can use business process automation to eliminate manual data entry, accelerate time to value, and eliminate unnecessary labor time spent on rote processes. Grab, a leading O2O (online to offline) company that combines e-commerce with ride-sharing and food delivery, and which notably bought out Uber’s operations in Southeast Asia, uses enterprise automation to cut internal operating costs and accelerate time to value. 

How Grab cuts internal costs and streamlines processes with automation

Provisioning IT assets can slow time to value if the process is inefficient. Grab solved this problem and accelerated time to value by using Slack as a ChatOps hub, where IT requests can be submitted, and securely approved. Grab also automated their employee onboarding process, to further accelerate time to value. They can now auto-provision Windows machines for new employees, pre-loaded with the appropriate applications, in under 20 minutes. 

Within a few months of implementing enterprise automation to streamline business processes at their organization, Grab had already saved 3000 labor hours across the company. They’ve also reduced human error and elevated the employee experience.

How to Automate Compliance Processes

72% of respondents to Accenture’s 2019 Compliance Risk Study report having quantitative cost reduction goals are targeting reductions of more than 10% over three years. Organizations can cut costs by introducing automation into the compliance processes to identify risks and red flags early on, and to ensure maximum accuracy for the single source of financial truth for your enterprise, whether it’s NetSuite or another ERP.

Improving data accuracy in your ERP

Having accurate data in your ERP is a crucial facet of compliance, and it’s important for processes like audits and financial risk identification. To improve data accuracy in your ERP, organizations need to optimize the process of synthesizing data from sources like eCommerce, payroll, CRM, and procurement apps, using automation. Automation eliminates manual data entry, which is error-prone. An enterprise automation platform can accurately and instantly sync information into your ERP, handling any necessary data transformations between applications, and it can do so without generating unwanted duplicates. 

Related: 5 reasons why procure-to-pay automation is critical to adopt

Defining and Refining Business Processes for Fintech Accelerates Transactions and Approvals

In Ray Dalio’s Principles: Life and Work, he describes how defining his decision-making criteria and using them as recipes for algorithms for future decision-making, which could be run as computer programs, helped him to make better decisions. Basically, he defined and refined his decision-making process, then automated decision-making, similarly to the way loan approval processes are being automated by fintech companies.

”Experience taught me how invaluable it is to reflect on and write down my decision-making criteria whenever I made a decision, so I got in the habit of doing that. With time, my collection of principles became like a collection of recipes for decision making.” “In fact, I was able to refine them [my principles] to the point that I could see how important it is to systemize your decision making. I discovered I could do that by expressing my decision-making criteria in the form of algorithms that I could embed into our computers. By running both decision-making systems- I.e., mine in my head and mine in the computer- next to each other, I learned the computer could make better decisions than me because it could process vastly more information than I could, and it could do it faster and unemotionally.” – Ray Dalio, Principles: Life and Work

Dalio’s meditations offer a philosophical perspective on the value of automation. 

Related: How to automate compliance processes with NetSuite

Implementing Process Automation for Fintech

Workato is an industry-leading enterprise automation platform that facilitates wall-to-wall process automation, orchestrated across SaaS and on-prem apps, cloud and on-prem databases, and microservices. Workato is trusted by companies including Grab, Mosaic, and Fundbox for enterprise automation.

To learn more about our platform, request a demo from our team.