Over 75 billion connected devices are expected to be installed worldwide by 2025.
AI spending is projected to reach $57.6 billion by 2021.
In addition, the current fintech adoption is estimated to be at 33% worldwide (up to 69% in some regions).
Each of the given numbers is impressive on its own. There is no denying that all three industries are currently on the rise. Yet, imagine the impact they can have when combined.
The convenience of IoT devices coupled with the power of data-fueled AI algorithms has groundbreaking potential. Yet, just like every technology has its benefits, all of them pose specific challenges startups should be ready to face.
If you are looking to build a fintech startup with a focus on AI or IoT, read this article first. It will shed light on the possible use cases for IoT and AI applications in fintech as well as the pitfalls you should consider when building one.
The internet of finance: How fintech companies can benefit from IoT
The primary benefit of fintech and IoT integration lies within the additional data channel it opens. By using smart connected devices, fintech companies can better understand customer behavior and tailor their product offerings accordingly.
Moreover, integrating fintech and IoT (i.e., personal wearable devices) will potentially make payments easier and safer.
The following will outline the most significant benefits obtained from the use of fintech IoT:
Wireless payments
Wearables have the potential to transform cash withdrawal and wireless payments by replacing traditional cards or smartphones with smart devices. In addition to popular smartwatches, there are several startups working on smart jewelry. Namely, Kerv positions itself as “the world’s first contactless payment ring.”
Security & authentication
Another use case for wearable IoT in fintech is security. For example, Nymi is a smart wristband that uses the person’s heartbeat (electrocardiogram) as the biometric authentication key. The technology was also tested for secure wireless payments back in 2015.
Risk assessment for insurance
Various IoT sensors can be an invaluable source of data for the insurance and risk management sectors. For example, car insurers will be able to offer personalized insurance packages, based on the client’s driving behavior, average speed, time and distance traveled, etc.
A good example of usage-based insurance is represented by Sierra Wireless.
Another car insurance startup, Metromile, offers a similar approach with its pay-per-mile insurance plans. Your vehicle can send any information directly to the insurance company to help document an accident or speed up the insurance payment.
A similar approach can be applied to smart home devices or health IoT devices, such as fitness trackers. Insurance companies will collect information about house maintenance or the condition of your health and tailor the insurance terms accordingly, or reward you for positive behavior.
For example, Beam Dental offers personalized dental insurance plans, based on the data sourced by their smart toothbrush.
On-site customer experience
Beacons can also improve the on-site banking experience with contextual information delivered straight to your iPhone (similar to the way retailers use IoT devices). This creates opportunities for personalized, tailored in-branch experiences.
By using such devices, bankers will be able to streamline their customer service and automate routine tasks. For example, they can send their customers to address the right specialist, depending on their questions, or help them navigate inside the bank.
Furthermore, NFC beacons can also be used to facilitate the checkout process and make payments easier.
How AI is changing fintech: The advantages and use cases for artificial intelligence in finance
The amounts of data sourced by applications as well as integrated fintech IoT devices can serve as a key enabler for better, smarter financial management. Furthermore, fintech AI use cases are virtually endless.
Feeding the users’ data to advanced machine learning (ML) systems can help you train the algorithms to optimize and automate many routine tasks. Thus, the processes that were once extremely resource-consuming will now become fully streamlined and more efficient.
Plus, unbiased, data-backed AI algorithms will eliminate the possibility of human error and minimize the overall risks in the sector.
The application of artificial intelligence in finance spreads across many areas, from personal banking to investment, asset management, and insurance.
Here are some examples of artificial intelligence in banking and finance:
Chatbots
There are several use cases for chatbots and AI in fintech:
- personal finance management and tailored financial recommendations (e.g., Penny – acquired by Credit Karma)
- budgeting and expense tracking (e.g., Olivia)
- savings (e.g., Digit)
- micro-investments (e.g. Plum)
- mobile payments and P2P money transfers (e.g., MyKAI)
Another widespread use case for AI in fintech, and specifically with chatbots, is customer service. By using smart AI agents, companies can speed up their response times and improve their issue resolutions rate. This has the potential to increase customer satisfaction and help brands increase loyalty.
Preventive cybersecurity and risk analysis
AI in the fintech industry can be used to identify and prevent various cybersecurity threats. Moreover, machine learning algorithms can conduct an unbiased risk assessment and detect suspicious behavior or transactions.
Source – cbinsights.com
Automated investment and asset management
Among the sectors that can benefit from smart automation, investment and asset management represent one of the most significant opportunities within the finance industry. According to BI Intelligence (the so-called robo-advisors), AI fintech agents for automated, algorithm-driven financial management, will be managing $1 trillion worth of assets by 2020 (and around $4.6 trillion by 2022).
Some of the most popular AI fintech startups in this sphere are Wealthfront and Betterment.
Things to consider when building a fintech app with AI or IoT integration
1. Get to know your market
When targeting a highly competitive industry, such as fintech, start with understanding the current market state and customer demand. What are the existing solutions? Which problems do they solve? Are they popular?
You need to know the competition in order to find a unique benefit that can set you apart from the rest of the market players.
Get to know your target audience – run a survey or create focus groups to talk to your potential users in person. Thus you will be able to identify the needs of your audience and understand if you can satisfy them with your product and how exactly you can do that. You can also learn from the success and failures of your direct and indirect competitors.
2. Pay attention to security
The applications that deal with user data, especially a financial one, must provide the highest level of protection.
Taking into account the amounts of information that IoT devices collect and the data that is needed to build and train AI algorithms, security should be your number one concern. Specifically, there are some major security risks when using automotive IoT devices or smart home devices.
With the introduction of the new data protection regulation in the EU, you should know exactly what data you collect, where it is stored and how it is used. Make sure you are aware of the possible legal implications and put the required security practices in place to avoid penalties.
3. Always think user-first
Most financial products, especially the ones that include IoT integrations and complex AI-based features, are usually hard to set up and use. It can be challenging for the users to understand how it works, which may breed confusion and low customer retention rate.
From your website to the app itself, make it easy to grasp the benefits of your product. Create a comprehensive onboarding UX to help users get started with your products. Your app should be easy to use and navigate. Include a detailed FAQ page or a 24/7 in-app support line.
4. Build an engaging experience
Personalization is another key element of a successful financial app. Having access to loads of customer-specific and real-time behavior data allows you to tailor the experience accordingly.
For example, you can push customized offers to your users depending on their activity. This can be a one-time discount for a purchase at a store you’ve partnered with, bonuses for specific actions within the app (e.g., inviting a friend or rating the app), cashback on every second purchase with a wireless payment.
5. Don’t overcrowd your app with irrelevant features
Keeping a financial app lean can be a challenge, especially if you are building a personal finance app. Yet, it is essential to keep the less important features to a minimum, at least at the beginning.
Building an MVP to test out your assumptions and improve/change/expand/pivot it in the future is a smart strategy that can save your time and money.
Build the future of finance with Eastern Peak
One more critical element of a successful fintech app is having a strong team by your side. In this case, it’s important to find a technology provider that has deep domain experience in fintech as well as extensive technical expertise in IoT and AI. This is precisely what we offer at Eastern Peak.
We have a solid fintech portfolio, and also specialize in building custom IoT solutions – from concept to hardware design and software integration. Hire a dedicated team to build your product on time and on budget.
For an estimate or professional consultation, get in touch using our contact form or call us right away at +1.646.889.1939.
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About the author:
Alexey Shalimov, CEO at Eastern Peak
As CEO at Eastern Peak, a professional software consulting and development company, Alexey ensures top quality and cost-effective services to clients from all over the world. Alexey is also a founder and technology evangelist at several technology companies. Previously, as a CEO of the Gett (GetTaxi) technology company, Alexey was in charge of developing the revolutionary Gett service from ground up and deploying the operation across the globe from New York to London and Tel Aviv.
