Meet Data: The Driving Power of Fintech

Off late, “Fintech” has been and remains to be a buzzword. It is transcending beyond traditional banking and financial services, encompassing online wallets, crypto, crowdfunding, asset management, and pretty much every other activity that includes a financial transaction. Thereby competing directly and fiercely with traditional financing giants and their methods.  

No wonder, the global investment in Fintech has, therefore, exploded in recent years. 

This Fintech boom is driven primarily by the availability of technologies like Big Data, Machine Learning, AI, Data Science, and Blockchain.  

The one common factor driving these technologies? DATA 

Now, we’ve previously spoken about living in a data deluge. Here we will talk about how Fintech’s leverage this data deluge or “alternate data” to augment conventional data sources to build robust solutions delivering valuable intelligence.  

Website traffic, product pricing, product reviews, and inventory data
available on e-commerce websites is extracted to understand how the company is doing in terms of growth. Apart from that the job postings, hiring trends, increasing or decreasing workforce size, company’s ratings on employer review sites, brand mentions on forums, and social media are extracted for more robust fundamental analysis. Sentiment analysis is then performed to gauge overall sentiment and feel good, inside and outside the organization. 

Buzz words aside, for venture capital firms to make intelligent and
informed investments and to maintain a better portfolio allocation. They need to understand the technology trends over a while, be confident of the
technology stack used by companies, and combine the two to make sound
investment decisions.

An important part of this analysis is the overall trends data gathered from tech portals, blogs from influential consulting organizations, posts on Reddit, competitions on Kaggle, and GitHub. Alongside the data collected from company websites, job postings, and yet again, social media posts and blogs— text mining and NLP (Natural Language Processing) techniques are applied on these data sets to match the trending topics and the way they change. Investors are then not solely relying on seemingly fascinating pitch decks but hard facts.

Rating agencies heavily monitor and extract data from the web for the companies they are tracking in their reports. This data primarily includes the public data on the company sites, their social media posts, media coverage, including brand mention and recognition, overall company activity, including hiring and layoffs.

While extracting data in real-time, this use case also requires sophisticated yet lightweight NLP and text analysis models to extract intelligence, intent, and sentiments from high volume and high-speed data being sourced.

It is paramount for companies to comply with regulatory requirements. Hence, for companies providing business consultation, advisory, audit, and risk mitigation services, they must crawl government sites and news outlets to stay updated with new policies being discussed and implemented. To not only advise organizations but equally importantly, be able to monitor the overall business, social, and economic impact of new regulations and guidelines, being implemented by governments.

Some business owners would risk having customers. Whether or not they can pay their bills can become somewhat of an afterthought for several businesses seeking aggressive growth. Until a lack of payment from customers becomes a cash-flow problem for their company. Assessing
the financial risk of each customer upfront can help avoid this problem. Being proactive and not reactive is the key.

Some of the insights that can easily be gathered from the web are: searching for your potential client’s website. Look at the Google Street View of the company’s address to see if their location makes sense based on their industry and size. You may also want to consider looking for feedback from other companies that have done business with them, and blog or social media reviews from their customers. The overall trend of the industry they operate in.

Then, of course, things like credit reports and bank credit references can be asked for further due diligence. Well again, this exercise need not be restricted to new customers you’re onboarding but should be extended to new business development when your sales teams are seeking new customers. Equipped with this information, they’d know exactly where to go/not go looking for prospects. Making your overall sales cycle a lot
more effective and productive.

Transcending from a communication platform, social media today, is a customer experience platform. Organizations engage in “Social listening” to find and contribute to conversations about them by seeking out brand mentions, specific keywords or phrases, and comments. A study by Gartner found that companies that do not respond to social media messages face up to a 15% increase in customer churn. Furthermore, an American Express study found that 83% of consumers abandon a purchase due to a poor customer service experience on social media.

Through social listening, you can increase revenue, earn higher customer satisfaction rates, and reduce customer support costs – everything you need to grow a business successfully.

By listening to conversations, companies can participate, educate, and build relationships with prospects and customers.

With so much to be achieved from web data,sourcing it reliably and at scale remains to be technically challenging and resource-intensive. What companies need is a technically adept partner with the know-how and the tools to build and execute data scrapers. To demonstrate instant ROIs and benefits for the organizations.