It’s pretty ironic to consider how automation is transforming the fintech sector when fintech transformed the financial sector.
Perhaps it’s testimony to the speed and pervasiveness of technological advances that the disruptors are being disrupted.
Transformation today is coming from hyper-automation, intelligent automation, or extreme automation. These terms mean that automation tools are very sophisticated and are being used in combination with each other.
Let’s look at how they are being used for fintech applications.
Automation for Fintech
Fintech companies have used technology to cut costs and time and replace traditional institutions. However, they must also provide quality, customer trust, and regulation.
Advanced automation tools provide the solution.
Going Beyond Robotic Process Automation (RPA).
RPA has been with us for a while. These are the “bots” that automate routine and rules-driven tasks. They are efficient, tend not to make human mistakes, and significantly cut the time and cost of repetitive tasks.
However, these bots can’t deal with complex, dynamic processes and workflows.
This is where Artificial Intelligence, Machine Learning, and Natural Language Programming come into play.
Advanced Automation Technologies
In these technologies, the software learns from itself and uses guiding principles to solve new problems.
Artificial Intelligence (AI)
AI is the science of using computers to mimic human intelligence. It is a form of advanced information processing.
For example, in the financial field,
- Algorithms analyze the history of risk, identify signals for potential issues, and detect fraud or spam.
- AI predicts borrower delinquency for credit authorization
- It has growing importance for investment and trading.
- Hedge funds use AI-powered analyses for market forecasting
- AI tests the parameters for multiple trading strategies before trading bots execute them
Machine Learning (ML)
Machine Learning is a subset of AI.
Systems learn from data, identify patterns, and use algorithms to predict outcomes. It is particularly useful for data on a large scale.
- It is the primary tool for Customer Relationship Management. It defines buyer persona and targets and qualifies leads.
- Virtual financial advisors offer personalized advice and answer queries based on past transactions and ML-generated questions and answers.
Natural Language Processing (NLP)
NLP is a subset of ML.
Computers now understand, analyze and even generate human language.
We know how Google can find relevant search results, translate pages from one language to another, or auto-correct spelling. NLP has now moved to recognizing speech and answering questions. It generates text from images or videos and detects sentiment.
All these functions are relevant to fintech.
- NLP reduces the time for paper trails in accounting and auditing.
- Analysts, traders, and portfolio managers undertake financial analyses based on text, documents, websites, and forums. NLP automatically converts text into insights and analytics.
- Sentiment assessment is used to judge the creditworthiness of previously unbanked people.
- NLP evaluates sentiment from social media, the financial press, and analyst reports to predict how the market will react to news.
- Entities involved in transactions such as loan agreements can be extracted using an NLP technique called Named Entity Recognition (NER).
Blockchain and Smart Contracts
A blockchain is particularly well suited to fintech.
It is a virtual ledger of transactions, tracing the transfer of ownership of assets. Records are authenticated by computers spread across the world and cannot be altered. Everybody has access to the same data in real-time.
Smart contracts can be attached to transactions, automatically triggering actions when pre-defined conditions have been met.
Hyper Automation for the Fintech Sector
Automation is even better when techniques are combined.
- NLP and ML together simplify the complex time series data needed for stock market predictions.
- Insurance and mortgage companies deal with multiple documents, some handwritten, barely legible, and in various formats. ML and NLP can process claims automatically.
- NLP measures changes to texts, compares documents, and identifies subtle market trends.
- At the same time, ML can separate payslips from bank statements and identify whether documents are missing or incomplete.
The ultimate combination is blockchain and AI. AI deals with large data sets, and blockchain provides security, trustworthiness, and quality to that data.
Ultimately, there will be trading on blockchain as the tokenization of assets becomes more prevalent and datasets grow. AI and ML will be used for pattern detection and predictive algorithms.
Advanced automation tools such as artificial intelligence, machine learning, natural language processing, blockchain, and smart contracts are transforming the fintech sector.
They bring both convenience and security to fintech applications and open the door for new products and services.