FinTech

Top Technology Trends That Will Impact the Fintech Industry in the Next 5 Years

Fintech companies are widely using biometrics to provide quick and secured financial services on-the-go
Top Technology Trends That Will Impact the Fintech Industry in the Next 5 Years
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Founder & CEO, Qbera.com
4 min read
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Millenials of India are reportedly losing interest in the traditional banking methods at a record pace. Conversely, fintech companies are grabbing the same millenials at a remarkable pace. This change largely is due to digital technologies that fintech companies are embracing and reshaping the way customers used to bank.

The success of fintech companies is hugely driven by their provision of seamless financial services online. This has been made possible with the introduction of new technologies that are making 'spending', 'managing' and 'investing' much more easier.

Even so, there are some technology trends that are set to revolutionize the Fintech Industry in the coming 5 years. Let's throw some light on those:

Blockchain

Blockchain is a structure wherein a financial transaction is broken down into encrypted 'blocks'. Each page of financial transactions forms a block which has an impact on another block. Each block, when completed, forms a unique security code and connects to another, eventually forming a 'blockchain'.

Fintech companies have started to use Blockchain technology to bring financial trust, transparency and security for customers. It is set to transform the digital identification process, cross-border payments and even trade finance platforms. Blockchain backs up the digital currency "Bitcoin" that has become the 'digital gold' of India.

Artificial Intelligence

Artificial Intelligence, or 'AI', is the process that involves identifying behavioral patterns of large amounts of recorded data. AI, with the help of machine learning can create algorithms which outperform human functions in banking e.g. credit decisions or re-marketing.

The data collected by AI or machine-learning algorithm includes a large amount of banking data which is written by each individual. With this data, financial institutions, majorly fintechs, form a set of queries for machines, rather than for humans. Machines come up with accurate predictions of customer behavior, based on all the data points provided. This helps in sorting what's right for the customer or even in identifying what the right time to give a loan to the customer is.

Biometrics

Fintech companies are widely using biometrics to provide quick and secured financial services on-the-go. Customers are increasingly putting trust into mobile banking after they went through significant levels of authentication while transacting. Fingerprint logins, facial and retinal scans have provided advanced authentication to customers looking for quick financial solutions and superior security.

Biometrics have been able to improve the user experience by slashing the need to upload the physical documents as identity proofs and photographs, and completely digitalize the know-your-customer or 'KYC' process. To give an example, the Unique Identification Authority of India (UIDAI) use fingerprint and iris data to provide authentication while issuing Aadhar cards to the Indian citizens. The unique identity card stores their personal information such as name, address, date of birth, gender, mobile number and email address. Moreover, UIDAI also provides a mechanism to lock the biometric information to prevent any misuse.

Alternative Data

While approving a loan, financial institutions consider the credit score of an individual to be the prime factor to judge his or her creditworthiness. A credit score can be influenced by other factors such as payment history, outstanding balances on loans, and recent new tradelines which have been opened, vintage of the file, etc. Lenders these days, however, want to know more about the customer's behavior - which often can't be discerned from a person's credit history.

Alternative data could include information from a person's social networks, bank statements, SMS data or e-mail inbox, which provide information on a person's authenticity, spending habits, cheque bounces, savings transactions, or other such information which can provide a more holistic view of a person's overall creditworthiness.

Natural Language Processing (NLP)

Fintechs have started to deploy NLP-services to reduce cost and improve services. NLP is a feature of Artificial Intelligence that understands written and verbal languages. NLP is implied in combination with sentiment analyzers to alleviate customer support communications.

Chatbots such as Siri, Alexa and Cortana are excellent examples of NLP. These are designed to feed contextual insights to the approaching customer. These bots are equipped to answer commonly asked questions such as:

? How much money did I spend on food last week?

? Which are the hospitals nearest to my location?

? Which movies are releasing this weekend?

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