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Harnessing big data allows financial technology (fintech) companies to provide personalized consumer interactions. However, effectively managing, analyzing, and extracting insights from vast datasets requires specialized skills and tools.
Thus, many fintech firms rely on business process outsourcing (BPO). Third-party service providers offer access to the resources necessary for big data analytics.
This article explores big data analysis outsourcing and its impact on operations. Keep reading to learn how fintech BPO can help with this task.
1. Increased leverage of cutting-edge technology
Fintech companies can use big data to predict customer preferences and develop tailored experiences that improve revenue and branding. According to a study by McKinsey and Company, 71% of consumers expect personalized interactions, and firms that offer them increase revenue by 40%.
However, managing and analyzing vast amounts of information to achieve personalization requires expensive, state-of-the-art technology. Fintech BPO providers typically invest in the most advanced big data analytics tools that offer the following advantages:
- Efficient data processing
- Real-time analysis
- Faster time to insight
- Proactive maintenance
- Diverse data type analysis (structured, unstructured, and semi-structured data)
Reputable service vendors leverage various platforms to seamlessly integrate with your existing systems and avoid compatibility issues and delays.
Here are some of the most prominent technologies you can access when partnering with an outsourcing team:
Cloud computing
Most BPO companies, such as those offering contact centers for e-commerce businesses, leverage cloud computing platforms.
Cloud technology lets users store, manage, access, and use vast data anywhere with an internet connection, eliminating the need for physical infrastructure. With it, you won’t have to worry about server limitations.
Some popular cloud platforms include Amazon Web Services (AWS) and Microsoft Azure.
Artificial intelligence (AI) and machine learning (ML)
Big data analysis outsourcing teams have expertise in building and deploying ML models for fintech functions through AI training. Together, these technologies allow them to automate repetitive processes associated with data analysis. These tasks include:
- Data anomaly detection
- Data classification
- Predictive modeling
- Regression analysis
- Missing data imputation
Additionally, ML models can uncover hidden patterns and relationships within complex datasets that human analysts might miss. The insights inform your decisions about your products and services, keeping you ahead of the competition.
Extract, transform, load and extract, load, transform (ETL/ELT) tools
Similar to ML and AI, this technology automates data extraction, transformation, and loading. These tools can obtain information from various sources, such as databases, social media feeds, and web applications.
It then transforms data into a consistent format for analysis and loads it into a warehouse or lake for specialists to easily access. Leveraging ETL/ELT platforms ensures data quality and streamlines the start of the analysis process.
2. Better access to global, specialized expertise
Data management and analysis are complex fields that require a specific skill set. Fintech companies might find it difficult to recruit the talent needed, especially with the continuous global labor shortage.
A study submitted to Research Gate supports this claim. According to the dissertation, the demand for big data professionals, including analysts, outpaces the number of experts in the field. Thus, maintaining a team for this task is challenging for fintech firms.
Fortunately, outsourcing lets you tap into a pool of experienced talent who can handle various tasks relevant to big data analytics, including the following:
- Fraud detection and prevention. This function involves real-time analysis of vast transaction data to identify suspicious patterns and prevent fraudulent activities.
- Credit risk assessment. BPO teams can help assess creditworthiness beyond traditional credit scores, incorporating social media information and more.
- Customer segmentation and targeting. Outsourcing providers often have experience analyzing customer data to understand their behaviors and preferences.
- Algorithmic trading and portfolio management. Big data analysis experts from outsourcing firms use ML to track fintech trends and inform decisions.
- Regulatory compliance. Similar to employer of record (EOR) providers, BPO teams help you comply with laws such as Know-Your-Customer (KYC) and Anti-Money Laundering (AML) requirements.
Besides these core processes, fintech BPO teams also handle supporting tasks such as:
- Data acquisition, integration, warehousing
- Data lake management and quality management
- Data wrangling, cleaning, transformation, and reduction
- Data communication and visualization
- Business intelligence (BI) reporting
- Cloud infrastructure management
- Ongoing maintenance for data pipelines, models, and infrastructure
Outsourcing big data analytics makes navigating the profession’s tight labor market more manageable. You have specialists who can handle the entire spectrum of big data analysis.
3. Boosted scalability for big data analytics operations
Fintech companies, especially startups, experience rapid scaling. This growth translates to higher data volumes and complexities, which can be challenging to manage and analyze. You might not have adequate in-house resources and staff for big data analytics.
The technologies and expertise offered by big data analysis outsourcing vendors empower you to easily adjust your fintech operations as needed. For example, the automation tools mentioned above enable you to quickly process data with fewer mistakes.
Additionally, fintech BPO providers offer the following scalability solutions:
Flexible engagement models
Fintech outsourcing firms offer various engagement models that allow them to tailor solutions based on your needs.
They offer tiered service packages, each providing different levels of data specialists and expertise. This model lets you choose a deal and easily upgrade to a higher tier as your business requires more resources.
Standardized solutions
Big data analysis outsourcing teams have well-defined workflows and best practices for fintech data ingestion, cleaning, analysis, and reporting. However, this doesn’t mean rigidity.
They can adapt to these frameworks while maintaining the core principles of data quality, consistency, and efficiency. BPO teams just have to adjust specific tools and orient teams to accommodate new aspects, such as:
- Data sources
- Analytical techniques
- Project requirements
- Industry-specific regulations
- Domain knowledge
Resources on demand
Fintech outsourcing providers have access to a global pool of pre-vetted data specialists with proven experience in big data management. This network enables them to source and deploy additional data specialists quickly as needed.
BPO firms also monitor their data team’s performance and adjust their composition based on results to ensure you have the right mix of skills. For instance, if you start using ML for fraud detection, the provider might add data analysts with that expertise.
4. Significant savings in overhead costs
Building and maintaining an in-house big data team is a massive undertaking, requiring significant upfront investments. Big data analysis outsourcing providers let your fintech company cut overhead costs through the following ways:
Eliminated infrastructure expenses
According to ITRex, a software development and information technology (IT) consulting company, building and implementing custom data analytics solutions cost up to $200,000.
By leveraging the technologies of outsourcing teams, you can avoid purchasing or subscribing to costly new hardware and software.
Some BPO providers offer pay-as-you-go models, which only charge you based on how many staff you employed and how long they worked. This model means you pay only for used resources.
Lower labor costs
Payscale reports that wages for data analysts go up to around $124,000 a year. BPO companies handle every aspect of talent acquisition and management for their clients, helping you save on costs associated with hiring and maintaining a team.
Coverage includes the following processes:
- Creating job postings
- Screening and interviews
- Hiring and onboarding
- Task-specific training
- Creating benefits packages
Additionally, BPO providers source talent in countries with lower living expenses and salary ranges. For example, according to Outsource Accelerator, businesses that outsource to the Philippines report saving up to 70% on labor costs.
Minimized office costs
Outsourcing often entails remote work. Cloud technology lets data analysts accomplish tasks anywhere with an internet connection. Online communication and collaboration tools also help make this arrangement effective.
Ultimately, a remote work setup helps you avoid the costs of maintaining a physical office, including:
- Commercial rent
- Electricity and other utilities
- Internet connection
- Waste management
- Furniture and equipment
- Security and maintenance
- Heating, ventilation, and air conditioning (HVAC) systems
The bottom line
With experienced big data analysts on the job and advanced technology to support them, you can focus on what your fintech company does best. You can also allocate the resources you saved through outsourcing to activities directly contributing to your business growth.
By partnering with a fintech BPO provider, you gain the strategic advantage of staying ahead of the fierce competition.
Maximize big data, gain valuable insights that inform your decisions, and develop innovative products while saving costs. Let’s connect if you want to learn more about big data analysis outsourcing and fintech BPO.
