- The Role of Big Data in FinTech
- Applications and Use Cases of (Big) Data Analytics in FinTech
- Why Does Your FinTech Business Need Data Analytics Outsourcing?
- Pros and Cons of Outsourcing Data Analytics
- How to Choose the Right FinTech Development Outsourcing Company?
- How can Appinventiv Help You with FinTech Data Analytics Solutions?
“Data is everything” is a well-established cliche and not just in the FinTech or Financial Services industry but in every possible business. But the real power lies in the ability to process raw data to produce tangible, actionable insights. What would it mean for your FinTech business to be able to process every little piece of data almost instantaneously and use it to improve customer experience and build better products faster than your competitors? That’s what data analytics does to your business.
Since the time FinTech witnessed the digital transformation, businesses efficiently utilized insights gained via data analytics reports to stop customer churn, prevent accidents, and predict and stop financial fraud and failures. Thanks to big data, FinTech has expanded its operations using modern technologies such as the Internet of Things (IoT), Blockchain, and Artificial Intelligence/ Machine Learning (AI/ML) , Smart data discovery. These applications are the primary reasons for SMBs and enterprises to prefer data analytics outsourcing.
Outsourcing is a cost-efficient strategy that helps startups, SMBs, and established enterprises effectively manage everyday operations by leveraging analytics expertise and curating data with their brand. We will discuss the pros and cons of outsourcing data analytics and how your FinTech business can outsource data management services in the latter part of our article.
First, let’s take a broader look at the role of big data analytics in FinTech.
The Role of Big Data in FinTech
The FinTech industry is growing at a CAGR of 6% and is expected to reach $28529.29 billion by 2025. The increased usage of mobile devices has contributed to more & more customers using FinTech products and services of some kind or the other. As these interactions and usage patterns are stored over time, users have an exponential increase in data produced every second.
This presents both challenges and opportunities for most FinTech businesses.
If your FinTech business lacks the ability to use this vast amount of data to cater to your customers, you are setting up for failure. On the other hand, advanced data analytics and smart data discovery capabilities can do wonders for your FinTech organizations.
According to a survey, over 66% of your consumers expect you to understand their expectations, and even more so, in the FinTech industry. In fact, 71% of FinTech users now demand more flexible journeys with multi-channel interactions.
If you have the required Data Analytics infrastructure, you can use this data to create customized products & features for your customers.
In a nutshell, (Big) Data in FinTech can help:
- With better customer segmentation to deliver more customer-centric products & services
- Solidify your fraud detection and risk management protocols
- Optimize your operational performance while reducing costs
- Outstand by accurately forecasting customer behavior and producing sophisticated risk evaluations
- Using consumer data to provide agile digital solutions
This brings us to our following discussion, how can your FinTech business apply data analytics insights to everyday operations?
Applications and Use Cases of (Big) Data Analytics in FinTech
The use cases of big data in online banks, FinTech startups, and long-established financial institutions are not just limited to converting data into valuable insights. You can actively use big data analytics in the FinTech business for a broad range of purposes. Let’s take a look at a few applications and use cases where pairing big data analytics with modern technologies benefits your FinTech business.
1. Big data in insurance
Traditionally, insurance companies used to rely on statistical and demographic data that is no longer relevant. They had to go through manual pricing on their policies and miss out on substantial financial opportunities. Modern insurance institutions take full advantage of big data to create low-risk insurance offers.
For reference, many car insurance companies use on-the-ground data and road accident statistics to estimate risk and adjust their policy offers.
2. Big data analytics in digital payments
FinTech actively uses big data and machine learning for fraud detection and security control purposes. The most recent digital transformation trend in online payments is the merger of payment processing with sales, where users can get loans within seconds. The online mechanism combines machine learning algorithms and big data to assess risk and the amount of credit available to the customer. This has proved to bring higher conversion rates to various FinTech enterprises.
For reference, various financial payment companies target young professionals and help them invest, save, and manage finance effectively and securely.
3. Big data technology in real estate
In order to sell more with a higher margin, real estate needs continuous market monitoring, dynamic pricing, and comprehensive information on properties. This includes capturing data from various sources and applying data analytics to make the right offers to the right customers. Therefore, big data analytics helps you minimize the risk of customers defaulting on payments.
4. Big data in lending
AI models and Big data analytics can be widely used in microfinancing and other lending businesses to make credit loans available to a broader audience. This brings you two benefits. First, your FinTech business would promote financial inclusion in your target audience, and second, the loan adoption would result in higher revenue to your FinTech business.
Moreover, the broader accessibility of instant loans boosts the economy and helps other businesses grow dynamically.
5. Predictive data analytics in wealth management
Wealth management is one area where predictive data analytics could be used to bridge the gap between business anticipations and customer expectations. The insightful customer data can be used to create more comprehensive customer profiles. Data also helps with customer retention; for reference, you can make customized offers for different demographics ranging from college loans to retirement plans.
Not just this, predictive data analytics aids with better customer segmentation, helps deliver more customer-centric products and services, and optimizes operations. This is probably why enterprises of every scale and size leverage data analytics services to enhance their entire customer process.
Now that we know how important data analytics insights can be to your FinTech business, let’s see why you should be outsourcing these data management services.
Why Does Your FinTech Business Need Data Analytics Outsourcing?
Effective data analytics needs state-of-the-art tech that is custom-made to serve the business needs of your FinTech organization. But if you are still not sure how outsourcing tech for data analytics can help, these points are for you:
1. Access to expertise:
You, as a financial service or a FinTech company, understand the nuance of delivering exceptional services in your domain. Seeking the required tech expertise of data analytics saves you the trouble of getting into a completely different vertical altogether with little to no experience.
It’s a fact that outsourcing comes with associated benefits like saved dollars and faster project delivery. Data analytics outsourcing is no different. Your FinTech company can save immense operational expenses with big data outsourcing since the team takes responsibility for the technical part of the business. This also reduces the overhead cost such as social security tax and payments associated with keeping an in-house development team.
Data analytics outsourcing in FinTech empowers you with the required adaptability, speed, and agility you need to outdo your competition. This factor not just allows you to maintain a smooth development process but also helps your business attain a global reach in the long run by providing stability.
4. Variety of development products:
When you outsource your business data demands and goals, there is a higher probability for your outsourced team to give you more than one solution. It’s easier to choose the highest viable solution amidst a variety of reliable results.
5. Tailored data analytics solutions:
The entire finance industry is established on data collection and analysis. By outsourcing finance requirements, you get access to customized FinTech software solutions that help them analyze customer sample data thoroughly. The software solutions result in increased sales and promote customer loyalty. This way, you can monitor every prospect carefully, right from credit scores to users’ buying patterns.
Besides, data analytics outsourcing companies are more likely to respond to every small detail in your business demand. However, every business aspect comes with its own set of pros and cons. How about we also cover some of the major benefits and drawbacks of big data outsourcing?
Pros and Cons of Outsourcing Data Analytics
As a fintech business owner, you must take into account not only the great opportunities but also the possible risks when it comes to outsourcing tech. Proper knowledge of merits and demerits will allow you to assess the picture more accurately.
Pros of outsourcing data analytics in FinTech
Attracting competent talent
The tech team you outsource will be an experienced professional in its area. They are more likely to cope with the highly specialized tasks and business requirements crucial in your FinTech development. Not only this, a study suggests that outsourced members are 41% more reliable when it comes to delivering timely results. This exhibits nothing but a competent team working for you.
Access to best technology and practices
Most FinTech startups and enterprises cannot afford to provide the latest technology to their data management team. Efficiently, a reliable outsourcing company that offers data analytics services would be competent in current technologies and practices since their primary objective is to offer you the most suitable solution.
Focus on customer-centric services
Modern customers expect companies to understand their expectations, needs, and demands. An outsourced team primarily relies on customer research data and enforces in-depth holistic strategies to develop a product that targets user needs from every angle. This helps you in delivering more customer-centric services and solutions.
Innovative technologies give tremendous opportunities and benefits to financial business institutions.
These include real-time payment processing, cloud computing, wealth tech, cryptocurrencies, blockchain, chatbots, advanced analytics, etc. Imagine the number of solutions you can leverage for your business using these technologies. This way, outsourcing tech is a reasonable benefit for exploring such new technologies and creating a new scope for your financial enterprise.
You may also want to read, The top 5 trends shaping the future of data infrastructure in 2022 and beyond
Cons of outsourcing data analytics in fintech
There are no major drawbacks to data analytics outsourcing except a few factors that can be overcome.
Misunderstanding business objectives
Your FinTech business might be associated with nuances that need to be clarified with your outsourced team. The right solution is to choose a service specializing in your specific industry business.
It is of utmost importance to ensure that your data analytics outsourcing company protects your data and safeguards against unauthorized access. This can be managed by signing a nondisclosure of information agreement with the company.
This brings us to our last segment;
How to Choose the Right FinTech Development Outsourcing Company?
Outsourcing FinTech needs is now more of a necessity than just an option. While you are at it, it’s crucial to understand how to outsource data analytics. Below are a few essential tips to keep in mind when outsourcing.
1. Work experience
The primary factor to consider is the reliable experience in developing high-quality FinTech solutions. Keep a note of previous FinTech projects that the company has worked on and their success rate. The portfolio of the outsourced company must guarantee reliable services, easy integrations, and maintenance of data analytics solutions.
Security identification and authentication are yet another critical factor to consider for outsourcing FinTech data analytics needs. FinTech is one area that requires great precision and safety in all operations and data. Therefore, make sure that your outsourced team prioritizes security and not as an afterthought.
3. Risk mitigation
Risk management is a primary decision-making plan to evaluate, identify and overcome risks to minimize the possible losses. Not to forget that your business data would be at stake; hence, ensure that your outsourced team should be ready with a backup plan, just in case your primary development plan fails.
How can Appinventiv Help You with FinTech Data Analytics Solutions?
As the big data analytics outsourcing market keeps gaining popularity in FinTech, it’s safe to conclude that soon it will become the lifeline of prominent startups and enterprises. With the help of big data, your FinTech organization will be more adept at providing customers with seamless service on every channel. Over several years of development, Appinventiv has successfully delivered data management solutions.