Digitization in the finance industry has enabled technology such as advanced analytics, machine learning, AI, big data, and the cloud, to penetrate and transform how financial institutions are competing in the market. Large companies are embracing these technologies to execute digital transformation, meet consumer demand, and bolster profit and loss. While most companies are storing new and valuable data, they aren’t necessarily sure how to maximize its potential, because the data is unstructured or not captured within the firm.
As the financial industry rapidly moves toward data-driven optimization, companies must respond to these changes in a deliberate and comprehensive manner. Efficient technology solutions that meet the analytical demands of digital transformation will enable financial organizations to fully leverage the capabilities of unstructured data, discover competitive advantages, and drive new market opportunities.
But first, organizations must understand the value of big data technology solutions and what they mean for both their customers and their business processes.
You have probably heard the term “big data.” If you are wondering what big data means, you are not alone. Generally, big data refers to large data sets, collected by firms and governments, that are so large and complex that traditional data processing methods are inadequate to deal with the calculations needed to make sense of the data.
These data sets are extremely valuable because of the vast information hidden within the data structures. When analyzed computationally, big data can provide a more precise insight into hidden patterns, trends, and associations, especially in the context of human decision making.
To deal with these challenges, researchers have developed what is called “predictive analytics” or “user behaviour analytics” to manage big data. Within these analytic methods, a variety of statistical techniques, including predictive modelling, machine learning, and data mining, can be used to extract value from the data. The strength of these methods lies in the creation of learning algorithms that find patterns that have predictive power.
Often, this involves analyzing trends in historical and transactional data to make new discoveries or predictions about future or other unknown events. Big data methodologies are currently being used throughout the financial services profession in areas such as actuarial science, marketing, insurance, health care, portfolio management, retirement planning, risk assessment, telecommunications, retail, and fraud detection.
Much of the value generated by big data focuses on four primary outcomes:
- helping firms cut costs;
- assisting firms to quickly respond to shifts in volume of data
- allowing managers to detect problems or system failures within a business structure; and enhancing managerial decision making.
For example, credit card issuers use big data analytics to detect fraudulent behaviour in a cardholder’s account before a statement is issued. Casinos use big data statistical techniques to stream coupons to app users while a gambler is making bets. Traders use big data to anticipate market shifts in real-time. Governments use big data to predict the people cheating on their taxes.
What is Big Data in Finance?
Big data in finance refers to petabytes of structured and unstructured data that can be used to anticipate customer behaviours and create strategies for banks and financial institutions.
The finance industry generates lots of data. Structured data is information managed within an organization in order to provide key decision-making insights. Unstructured data exists in multiple sources in increasing volumes and offers significant analytical opportunities.
There are billions of dollars moving across global markets daily, and analysts are responsible for monitoring this data with precision, security, and speed to establish predictions, uncover patterns, and create predictive strategies. The value of this data is heavily reliant on how it is gathered, processed, stored, and interpreted. Because legacy systems cannot support unstructured and siloed data without complex and significant IT involvement, analysts are increasingly adopting cloud data solutions.
Cloud-based big data solutions not only cut costs of on-premise hardware with limited shelf life but also improve scalability and flexibility, integrate security across all business applications, and — most importantly — garner a more efficient approach to big data and analytics.
With the ability to analyze diverse sets of data, financial companies can make informed decisions on uses like improved customer service, fraud prevention, better customer targeting, top channel performance, and risk exposure assessment.
Financial institutions are not native to the digital landscape and have had to undergo a long process of conversion that has required behavioural and technological change. In the past few years, big data in finance has led to significant technological innovations that have enabled convenient, personalized, and secure solutions for the industry. As a result, big data analytics has managed to transform not only individual business processes but also the entire financial services sector.
How Big Data Can Play An Essential Role In Fintech Evolution?
Fintech companies are well-known for being customer-focused, with customer segmentation being one of their interest areas. The financial industry is focused on dividing their customers depending on age, gender, online behaviour, economic status, and geographical coordinates. In this regard, fintech companies can easily analyze spending habits depending on age, gender, and social class. They can also easily tailor their services and alternate banking products to meet the demand and needs of each customer segment. The most valuable customers, namely those spending the most money, can also be identified. This will generate higher levels of customer satisfaction, as people generally seek highly personalized offers and financial products.
In the banking and fintech industry, like in many others, offering personalised services is one of the greatest marketing tools available. Alternative banking institutions began to use the services of fintech companies to improve their services and offer more personalized packages, but also a better, more comprehensive, faster infrastructure, which contributes to creating a more personalized and facile experience for the final consumer.
The pressure to create personalized services in the industry is also driven by the increasing number of companies that adopt such strategies, thus where a keen competition is present. Not only can fintech companies identify spending patterns to make banking recommendations, but they can also use those to help the final user save more money if this is one of their goals. Unlike traditional banking institutions, fintech companies focus more on creating personalized financial services that meet the very specific demands of the final consumer.
Another advantage of using Big Data in the financial industry is the fraud detection prospects that it opens. Obviously, with the rise of online banking and internet transactions, companies in the sector and their clients are more susceptible to fall victim to fraud. Big Data helps banks and other financial institutions to better understand the spending habits of each customer, but also their usual online patterns. In this case, when unusual activity is detected by the enterprise, the holder of the account can easily be contacted, asked and/or informed about a transaction that seems suspicious.
Obviously, risk management is an area of high interest in all industries. Once again, in the finance industry, Big Data comes with the immense advantage of identifying potential risk in terms of bad investments or bad payers. While Big Data cannot completely prevent such risks, it can identify those at early stages and prevent further development into risky paths. Big data can help companies in the financial industry tailor programs and strategies that will assess the potential risks and minimize those. Financial institutions have always had a huge amount of information stored in their databases but do not maximise it in the best possible way. The emergence of technology in the evolution of the Banking & Finance Industry has however presented the importance of data and data analysis and the opportunities associated with them. Now, banks can convert their huge amount of data into meaningful benefits for themselves and their customers.
Data Challenges for Financial Institutions.
- How do you reach the unbanked at the bottom of the pyramid?
- How do you expand the reach of your retail banking?
- How do you effectively track banking activities?
- How do you increase turnaround time in the banking halls?
- How do you increase efficiency in the banking process?
DataBeaver for Financial Institutions
Databeaver is a tech solution built to solve these common issues peculiar to the finance industry. DataBeaver is an integrated data collection platform that provides your organization with a means of capturing and analysing different classes of data such as;
- Biometric Data
- Geographic Data
- Demographic Data
- Images and Video Data
The data is collated via logic based smart forms that present them in an easy way to interpret, analyse, and make better, informed decisions with.
Precision, Decision, Action
Precision - Databeaver ensures data is collected in the most reliable and precise manner; it guarantees a high level of data integrity with features such as double-stage validation, Geo-stamp and Time-stamp.
Decision - Data captured is analysed to generate insights that will ensure your decision-making process is well informed and guided.
Action - Take more targeted action with insight from data.
DataBeaver Solutions For Financial Institutions
- Automate Marketing and Sales Effort: Digitise all paper forms
- Insights For Informed Decision-Making: Conduct customer surveys on brand perception, customer spending patterns and retail banking activities
- Customer Enrolment and Verification: Institution agents can use Databeaver to do an end-to-end enrolment on the field. Any kind of data (image, biometrics etc)
- Transactional Modelling and Customer Targeting: Use data to inform and drive customer targeting and generate the models and insights that can help drive more targeted marketing efforts.
- Improved Customer Service: Improve turn-around time in banking halls by introducing self-service support in banking halls to perform activities such as account request, Balance check, BVN updates, etc.