Gig workers can be considered the backbone of our modern economy. Just think of how many times you’ve been saved by a rideshare driver, food delivery worker, or freelance specialist assisting with a niche task. However, more often than not, gig workers are left behind when it comes to financial services.
Due to the non-traditional nature of their employment and inconsistent incomes, they can’t always access savings, credit, or fair financial tools. These tools are simply not quite tailored for them.
Enter open banking paired with AI. This cutting-edge technology could offer a lifeline to gig workers managing their finances, giving them power and control over their financial data. We’ll explore how open banking and artificial intelligence are building a more inclusive future via real-time income tracking, personalized budgeting, and predictive cash flow modeling.
The Freelance Future Is Already Here
The term “gig work” falls under the broader umbrella of non-standard employment (NSE), which includes work that typically varies between temporary and contract work. While not all NSE is short-term or remote, and there’s a difference between gig work and freelancing, lots of gig work shares these characteristics. In recent years, the growth of people employed in these types of jobs has boomed. These changes are indicative of an immense change in the workforce.
The following trends were found:
- In the United States, approximately 38% of working Americans, around 64 million people, made up the gig economy.
- Globally, the number of gig workers makes up 12% of employed people. This number has grown significantly compared to previous years.
Despite the clear surge in people opting for alternative employment structures, financial tools are still only catering to the traditionally employed. Banks and financial tool systems only benefit full-time employees with consistent and predictable paychecks.
However, these workers are no longer the norm.
Gig workers may find themselves struggling to get their loans approved, access to credit cards, and saving tools are proving to not be personalized enough for their needs. This may be due to them being perceived as financial risks, despite having steady incomes.
This issue begs the question: How do we build financial tools and platforms for people who don’t receive scheduled payslips?
The answer could lie in open banking and AI joining forces, technologies that are currently reshaping the financial landscape under our noses. Open banking offers users much more control over their finances; it allows for financial data to be safely shared with apps tailored to assist with specific needs. AI adds intelligence to this data, engineering tools like income predictions, improved risk assessments, and ultimately, personalized financial services.
What Is Open Banking and Why Should You Care?
Open banking refers to the practice of allowing your financial data (think statements, income, and spending history) to be securely shared with trusted services or apps that, with your strict permission, assess your data to help you manage your money. This is done through the use of application programming interfaces (APIs).
Imagine it like logging into Spotify with your Facebook account, but for your money. You’re giving two secure and trusted applications access to both accounts so that they can communicate and make your life easier.
Some of the top and widely used API companies include:
- Plaid (U.S./global): Plaid’s platform can seamlessly blend with apps like Venmo, Chime, and Robinhood, in addition to thousands of banks.
- TrueLayer (Europe/UK): TrueLayer offers a range of payments, identity, and data services and has proven to be a popular choice among fintechs based in the UK and EU.
- Tink (Europe/Global, acquired by Visa): Tink provides customers and major banks open banking infrastructure across 18+ markets in Europe.
How do open banking API systems work?
APIs such as Plaid or TrueLayer act as a data bridge for the user to securely link their bank account to a FinTech app via a consent screen.
Banking Systems vs. Gig Worker Realities
Gig workers’ employment details are incredibly varied. There are a million different ways they produce an income; how often, how much, and where their money comes from. However, there is one clear, common denominator: it’s likely inconsistent. This is the major aspect that sets them apart from traditional employees.
A 2021 paper unpacking the financial behaviors of gig workers produced very interesting findings. They noted distinct financial challenges, such as:
- Lack of medical insurance benefits
- High loan usage
- Financial-related stress
- High reports of debt
Despite this, gig workers reported having very high financial confidence. This reinforces the idea that they’re not reckless or financially uneducated, but traditional financial models simply do not work on their terms. AI and open banking can bridge this gap that gig workers are struggling with; i.e., traditional banks not being able to assess their financial risks and their potential lack of credit history and saving tools.
How Open Banking Unlocks New Financial Tools
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Unified income view: Open banking does exactly what gig workers might be struggling with. It allows users to link more than one source of income and bank account, which could be revolutionary to people who have multiple streams of income. This creates a much more condensed, comprehensive view of a person’s accumulated income, even if its source is a variety of platforms. As a result, this is a lot more presentable to banks and lenders.
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Real-time verification: Providing income proof can be challenging for gig workers, it can be tricky and laborious. Rather than submitting income proof and waiting for it to be verified, fintech apps utilize APIs to analyze the most recent transactions. This provides a much more efficient, hassle-free process for gig workers and provides accessible financial services.
For instance, some apps do not require credit checks to offer short-term advances but rather look at bank movements and data. EarnIn allows users to use portions of their income ahead of being paid, through open banking closely monitoring income flow.
- Smarter credit scoring models: Open banking analyzing bank transactions can also make credit checking easier. By doing so, alternative credit profiles are created. As opposed to traditional credit assessment models, open banking rewards responsible financial behavior and consistency. This opens up a world in which users can better qualify for improved rates, credit, and loans.
- Smart automation: Automated financial tools can greatly relieve budgeting stressors, especially with an irregular income. Here’s how open banking and fintech tools like Cleo, Even, and Steady are doing it:
- Detects when money enters your account and sets some aside accordingly for taxes or savings.
- Inform you with smart alerts or slightly adjust your spending plan once your balance is predicted to be lower than usual.
These are incredibly useful ways users can gain control over their money when it becomes difficult to predict their income.
AI Meets Open Banking: Smarter Tools for a Smarter Workforce
AI, combined with the open banking infrastructure, creates an incredibly personalized and adaptable tool, capable of even predicting users’ financial habits. This kind of technology is not only convenient for people like gig workers but essential.
AI + Open Banking = Personalization
Looking at the practical applications of AI algorithms paired with open banking APIs, the outcome is a hyper-personalized tool that can give clever, proactive suggestions.
For instance, a notification alerting you of a third consecutive low-income week and providing you with personalized fund-saving suggestions, such as minimizing subscriptions. Apps like Cleo incorporate predictive models, and instead of offering damage control, they can assist users before they reach financial strain.
Alternative Credit Models:
Traditional credit models cannot cater to the gig economy, as they require static variables of payment transactions, credit scores, and credit utilization. Earlier we looked at the role that open banking has in alternative credit models by evaluating transactions. AI takes this up a notch with improved, more adaptable models using financial data to evolve, recognizing patterns and habits, and responding to them.
Via an open banking platform, AI can analyze:
- Income flow consistency
- Variety of income sources from different platforms
- Recurring bills consistently paid
Chatbots and Financial Coaching:
Conversational AI can offer an affordable, human-like platform to discuss finances and give suggestions. These chatbot features can help you save, budget, and receive financial advice. While not designed to replace human advisors, this could be a helpful tool.
Risk & Fraud Detection:
AI has become very advanced at detecting irregular behavior and patterns. Among the many AI capabilities include preventing fraudulent transactions, flagging suspicious activity, and spotting unusually high spending.
Developer’s Perspective: Building for Financial Fluidity
Building fintech tools that include the needs of gig workers is an architectural challenge. A new approach to design, data, and UX is needed to create software that understands the complexities of income paid on random days that come from different platforms and includes tips and bonuses sporadically.
Technical challenges:
- Fragmented APIs
Every NSE platform could have very different API processes, if any. This calls for extensive mapping, expert error-handling, and consistent attention and maintenance on these systems to ensure a successful accumulation of data across banking accounts and gig platforms.
- Varied incomes
Developers need to normalize income data from inconsistent earnings; however, this is understandably much more complex than building for traditionally salaried employees. A fluctuating cash flow model will be essential when building for savings or budgeting tailored for gig workers.
Engineering possibilities:
Building tools that benefit and assist gig workers would be essential for the gig economy. Imagine it’s like designing a GPS that updates every 3 seconds, thus it’s crucial to constantly adapt to remain useful.
The logic behind features like income-smoothing dashboards, lending principles based on income flow, and predictive budgeting could greatly empower people instead of penalizing them.
Automating transaction labeling could create a more seamless integration with banks by making use of open-source fintech stacks. This might make it a smoother, more reliable process to recognize different streams of income, resulting in improved user and reporting insights.
Conclusion: Building for a More Inclusive Financial Future
The gig economy indicates a fundamental shift in how people earn a living. Millions of people are opting for flexible employment that provides creativity, autonomy, and diversity, despite their financial services and tools not serving them.
The infrastructure of open banking paired with AI has the power to close this gap, where open banking empowers users with control over their financial data. The combination of AI then renders that data useful with predictive models, dynamic personalization, and real-time analytics.
Developers certainly have a unique opportunity to reshape rigid financial systems around current world behaviors. The question is, how are tools developed to adapt to volatility, as opposed to eliminating it? The freelance future has arrived, and it’s time that our financial tools reflect that.
