FinTech – For sure, the financial industry is using AI and machine learning to increase returns, lower costs, and improve customer experiences. The companies that are still not open to welcoming and adopting changes brought about by new electricity called artificial intelligence will slowly die because there is no power. Quantum computing will add more power to AI and the next generation of wireless networks.
The questions that remain are: how far will IOT, AI, ML, etc. go in FinTech, MFS, and banking? How can other sectors, such as banking and investment banking, leverage this emerging technology? Of course, cybersecurity will become the biggest and greatest challenge of all time.
Artificial intelligence & FinTech
Artificial intelligence is poised to transform the financial industry. This intelligence will be built on modern software platforms that combine data from different sources. Data sources can be from a platform that generates data, i.e., social media, or from a data collection platform, i.e., the back-end system of any industry.
So for responding to the two main questions mentioned below around AI in FinTech,
- Where do all these AI trends fit into the fintech and finance industries?
- How will the machine learning algorithm transform financial technology services?
We need to go into little details about finance, banking, MNO-provided financial services, financial technology, blockchain, AI, machine learning, and data science to put them together in one photo frame.
Banking
Digitization gave banks the opportunity to take customer service to the next level. while at the same time offering the possibility of higher automation. The related cost efficiencies are now taken care of by AI, chatbots, and machine learning.
Essentially, digitization took banks from just being product providers to a service offering, providing continuous contextualized services and helping customers better understand their financial and commercial affairs and make smarter decisions.
MNO led Financial Services
Banks lost out on revenue from mobile transactions as MMS became increasingly popular.
Major success factors of MMS are the flexibility to transact at any time, anywhere, and with access to make payments to utility bill companies, airtime sellers, and merchants. To maintain relevance, banks started working on technology-based payment solutions in collaboration with card companies and opened their doors to all customers and services.
This brought about the merger of mobile money services, mobile financial services, and mobile banking services. With this fusion, cross-border remittances, peer-to-peer transfers, utility bill payments, internet and DTH subscriptions, and income tax transactions are completed within seconds.
Little History on Financial Technology – FinTech
FinTech, or financial technology, is an industry combining and grouping companies that are making financial services, including insurance services, more efficient and advanced with the use of technology.
FinTech Evolution: Companies coming into the FinTech domain are startups founded with the purpose of disrupting incumbent financial systems and corporations that rely less on software but more on banking as a platform (Baap) and banking as a service (BaaS).
Artificial Intelligence and Machine Learning in Finance, Regulation, and Central Banking need to be taken seriously and adapted as a basic need. Sadly, to date, most machine learning algorithms today cannot offer reason. At the same time, all of them predict that the past will be repeated if all other factors in the business environment remain constant.
Blockchain and Digital Payments – PaymentIntelligence
A blockchain is a new approach to managing and monitoring financial and other transactions. The main features of blockchain are
- Shared one point of truth
- Immutability
- Non-repudiable: This means once the transaction is committed, it cannot be denied later.
Digital Payments: The digital transformation of payments into secured payment intelligence was the biggest breakthrough after the 2008 financial crisis or recession.
One View – AI, ML, Blockchain & FinTech
To put all of them in the picture is not an easy job, though. We don’t have any successful models that have all these niche technologies in the photo frame. Lots of banks and FinTech are working to bring this amazing product, which is fully functional and working, to life.
Banking as a Service (BaaS) This needs time. This will have the security of blockchain, the intelligence of machines and algorithms, and the eyes of AI. To draw the rough sketch just to give one view, it might look like the one below.
Deep learning methods are helping time-series data processing and prediction for financial markets. These techniques for traditional feature extraction via intelligent trading decisions are supporting the FinTech system. The techniques used are applied to several technical indicators and expert rules to extract numerical features. An algorithmic trading framework with the use of deep convolutional neural networks is a good place to start for FinTech.
Today’s need for the payment industry is payment as a service (PYaaS). PYaaS, or what I now call Payment Intelligence, can solve current issues like high cost, slow speed, and bundles of papers for KYC. The new concept of BaaS with AI will revolutionize the market. We have too many payment companies in the market with zero or no experience but still doing well on a gratitude basis, as talent and education may not be needed here. Banks have platforms, but it’s probably fair to say it’s more like yesterday’s platform. What is needed is an intelligent banking platform on the cloud.—
Books + Other readings Referred
- Research through the open internet: news portals, economic development report papers, and conferences
- Personal experience of @AILabPage members.
Conclusion- The world of financial services, which encompasses the insurance sector, is on the brink of experiencing significant technological changes. In the coming years, it is envisaged that the FinTech industry will utilize the latest techniques and algorithms within the deep learning framework. This could have creative uses in upcoming financial technology advancements. This disturbance has already caused significant changes in the banking and payment industries.
