AI and Big Data for FinTech & InsureTech

Big Data: Last Sunday, I was at the big retail store in Harare. It was a busy day because it was month-end and people got paid. Grocery shopping was in full swing, and I also bought some groceries for myself. When I was in the queue for payment and collection, I saw almost everyone making payment either by swiping the magic plastic card or struggling on their mobile handset by punching a few numbers, etc. AILabPage team did some brainstorming session on same. In this blog post you and I will walk through some of such exciting scenarios.

Cash Payments vs Electronic Payment – Source of Big Data

A smart BigData factory should take a smart approach to costly, sensitive, critical asset and maintenance for data management. During our in lab brainstorming session what was evident was;

• The electronic payment queue moves faster compared to the cash payment queue.
• Card swiping and mobile payments generating a huge amount of data.
• The scene in the store looks like data production factory running restlessly.
• What was happening in that store beside the payments?
  • Data, More Data, Lots of Data so called BIG DATA was getting generated.

Now if we relook at above points to think about information security and customer privacy. We will realise that without the right Information-Security and encryption solution in place; big data is a very big problem as well. What AILabPage propose and advocate for is data intelligence with DataSecurity as prime goals for any data factory.

What is Big Data?

“What is Big Data”. I am sure most of us know the answer already; “A term used for the huge amount of Digital Data which is mostly in the unorganised and unstructured format because it is captured from different sources”. To expand further, big data refers to extremely large and complex data sets that cannot be effectively processed using traditional data processing applications. These data sets are typically characterized by the three Vs:

1. Volume: The sheer amount of data generated or collected, which can range from terabytes to exabytes and beyond.
2. Velocity: The speed at which data is generated and processed. With the proliferation of the Internet of Things (IoT) and other real-time data sources, data is often generated at an unprecedented speed.
3. Variety: The different types of data formats and sources, including structured, unstructured, and semi-structured data. This can include text, images, videos, sensor data, and more.

Big Data is so big that it makes it difficult to analyse. For instance, cardholder data should be managed in a highly secured data vault, using multiple encryption keys with split knowledge and dual/triple control.

Artificial Intelligence

Artificial intelligence and its bundle technologies and techniques made data security work and process easier. AI is able to map dummy data to real data to mislead hackers. A data thief would not be able to make use of information stolen from a database without also having multiple levels of keys. Big data presents a tremendous opportunity for enterprises across multiple industries, especially in the tsunami-like data flow industries, i.e., payments and social media.

The data we generate as customers or users on various platforms is kept safe and used at the data collector’s discretion. As consumers, do we have any control over it? (Remember Mark Z’s statement, “If you are not paying for the product, then you are the product”). Some of the questions are

• Who owns this data?
• What is the use of this data?
• How secure is this data?

FinTech, Social Media, InsureTech, and MedTech are major data-generating industries, i.e., massive groups of factories. The cost of any business is high in today’s competitive environment. In order to keep any business alive, it’s important to make the best use of data, but it’s important to note that ethical use of personal and private data is part of data science engineering.

Data from Google shows technology-based innovative insurance companies pay $0.60–$0.65 in claims against each one-dollar premium, with the rest covering the costs of administration, marketing, and reinsurance.

Information Security, Big Data and Artificial Intelligence

My payment data and all my sensitive information are secured and in safe hands. What about the privacy of my sensitive information? Thousands of questions started spinning in my head. There is a massive scope for big data security. This presents a significant opportunity for disruption. Improvements in technology, which are happening every day without demand, will bring a reduction in each of these cost items.

More startups are coming in to disrupt this massive and antiquated industry. Artificial intelligence helps reduce underwriting risk using big data and machine learning; it also offers secure data migration to secured data vaults. Automating policy administration and claims payout to bring a big smile to customers’ faces and improving distribution via marketplaces The wide variety of data volumes generated by FinTech, InsureTech, and MedTech is inspiring for data scientists (I simply love this and would feel very happy to play with it if I ever get access to it), executives, product managers, and marketers.

Leveraging on data from different platforms, i.e., CRM platforms, spreadsheets, enterprise planning systems, social media feeds like Facebook, Twitter, Instagram, LinkedIn, the company website feed section, any video file, and any other source Thanks to mobile devices, tracking systems, RFID, sensor networks, Internet searches, automated record keeping, video archives, e-commerce, etc., more information is derived by analyzing all this information, which on its own creates another enormous data set.

Data Sources and Data Security

New data sources create more data, which requires new processes to handle, and that in turn creates new security vulnerabilities. Over-the-air provisioning of payment credentials and applications is the main reason and biggest example of the potential for attackers to create vulnerable vectors for unwanted listeners to steal and misuse customer data. It is basically a process of infectious interaction with people and their environment.

In this era of dynamic digital payments and connected financial services, all businesses need to embrace evolution for growth and continuity. This isn’t just about one source of data from one payment system. To exploit the full advantages of big data, companies have to leverage various forms of data, including structured data in a range of heterogeneous applications and databases and unstructured data that comes in a number of file types.

The data warehouse can help in making more informed decisions, plans for growth, discover & design roadmap for new opportunities. This opportunity can be used for optimization or to deliver breakthrough innovations.

Big Data in FinTech and InsurTech

Today, we don’t know where new data sources may come from tomorrow, but we can have some certainty that there will be more to be content with and more diversity to accommodate. Big data factories are operating and pursuing analytics these days because it can be revelatory in spotting business trends, improving research quality, and gaining insights in a variety of fields, from FinTech to InfoTech to InsureTech to MedTech to law enforcement and everything in between and beyond.

Big data frameworks powered by Hadoop, Teradata, MongoDB, or another system—massive amounts of sensitive data may be managed at any given time. Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

Additionally, big data often involves the concept of variability, which refers to the inconsistency of the data sets due to the multiple sources from which the data is collected. Furthermore, the concept of veracity refers to the reliability and accuracy of the data.

Sensitive assets don’t just live on big data nodes; they can also come in the form of system logs, configuration files, error logs, and more. The environment of data generation itself has its own challenges, including capturing, curation, storage, searching, sharing, transferring, analysis, and visualization methods. Sources can include “personally identifiable information,”, payment card data, intellectual property, health records, and much more.

Data Analytics with AI Techniques.

Out of 3 well-known machine learning (a subset of AI) types unsupervised learning is most unused or least used technique. As in Unsupervised Machine Learning (UML).

• The idea to explore data is to look for hidden gems/patterns.
• To find some intrinsic structure in data.
• Something cant be seen with naked eyes requires magnifier (UML)

In unsupervised learning, available data have no target attribute. Machine Learning Algorithm takes training examples as the set of attributes/features alone. The most common unsupervised learning method is cluster analysis at the same time two general strategies in UML includes:

• Clustering – Partitions data into distinct clusters based on distance to the centroid of a cluster
• Hierarchical Clustering – Cluster tree is built with a multilevel hierarchy of clusters. No assumptions on the number of clusters
  • Agglomerative – In this technique, its start with the points as individual clusters as it moves forward; at each step, merge the closest pair of clusters until only one cluster left.
  • Divisive – Here its start with one, all-inclusive cluster. At each step, split a cluster until each cluster contains a point.

The system does self-discovery of patterns, regularities and features etc. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is the main target here. Since the examples given to the learner are unlabelled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.

The purpose of unsupervised learning is to attempt to find natural partitions in the training set. To AILabPage, UML is the most appealing machine learning type, and we consider this a treasure. Unsupervised learning is the main strength of the AILabPage team. We should not forget one of the biggest rules or laws here: the data generation sources need to be secured to address security policies and compliance mandates along with the data itself.

Conclusion – In short, big data has transformed artificial intelligence to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways, but for now, that’s for my next post. I guess my analysis is reasonable, but my conclusion at this time might be a bit premature. The clearinghouses get real-time payment facts to apply their ability and also have a vision beyond their current rails and the pockets to support it. Furthermore, more data sources are added all the time. Start and grow businesses and prevent fraud by putting security before innovation. Transactions and data generated out of them will then be safe, quick, and easy.

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Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around data analytics for digital marketing analytics. In the next upcoming chapters will talk about implementation, usage and practice experience for markets.

Books + Other readings Referred

• Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about  AI,  Machine Learning, Data Science or Big Data Analytics? Leave a question in a comment or ask via email. Will try best to answer it.

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