By Naina Bandyopadhyay
Traditional financial methods in the banking sector have been the norm for many years. As financial institutions embarked on the path of a digital journey, lots of data was generated and presented a situation where analysis and forecasting from the data were essential to survive and sustain in the field.
Tapping the business opportunity
A business model transformation became an opportunity for financial technology to manage these data and bring out financial solutions. Processing of complex information using algorithms and simulations was not enough and it eventually paved the way for the application of AI & ML.
Artificial intelligence (AI) and Machine learning (ML) in fintechare powerful tools for predictive analytics and decision-making. Process automation and digital transformation enabled FinTech companies to analyze endless customer data at a secure and faster pace and respond to personalized financial seek and fraud activities in a smarter way.
Benefits of AI & ML in FinTech
The arms of data science in the financial sector now extend to various sorts of Fraud detection, Risk management, Sales forecasting, Customer support, Asset Management, Service personalization, Product recommendations, Stock price prediction, and cybersecurity. Analyzing the voluminous data generated by everyday transactions and user engagements is extremely critical. Some of the key benefits of AI and ML in FinTech are:
- AI systems can monitor millions of transactions in real-time, and ML algorithms can detect any deviation from expected (fraudulent banking operations) more accurately and before the scam occurs.
- The machine monitors each transaction with the trained AI-ML process and acts immediately if it notices something anomalous. It provides an uninterrupted 24/7 working process thus reducing cost & saving critical time for decision-makers by automating the process.
- AL solutions like chatbots powered by AI, facial recognition, speech, and fingerprint identification have taken up security to the next level of reliability and have made the authorization process more robust.
- Customer profile, risk appetite, credit scoring, and disbursement decision, all are guided by insights from a large amount of data. AI-powered solutions like propensity to pay for credit card debt, overdue charges for accounts with a negative balance, etc, support debt prioritization and categorize the debt portfolio (Ranking the overdue invoices from cashless prescriptions according to the probability of payout).
- Data security, encryption, customer risk profiling, assessing bad debt, etc, are all now driven by robust analytics using AI and ML algorithms at the backend.
Future of FinTech
Personal banking apps are being developed for higher user engagement and that extends the benefits of predictive analysis to the customers. AI solutions (chatbots) monitor app users, respond to their questions instantly and provide recommendations and spending statistics of user preferences & activity patterns.
The use of AI and ML is the necessary solution in fintech as it is attributed to the abundance of volume, velocity, and value of the data that is extremely vital in financial sectors (3 out of the 5 ‘V’s of Big Data).From cybersecurity to customer service, AI has enabled digital easiness and comfort from both developers’ and consumers’ points of view.
1. How is AI/ML helping the fintech companies?
Artificial intelligence (AI) and Machine learning (ML) in fintech are powerful tools for predictive analytics and decision-making. Process automation and digital transformation enabled FinTech companies to analyze endless customer data at a secure and faster pace and respond to personalized financial seek and fraud activities in a smarter way.
2. What are the few applications of data science in the finance sector?
The arms of data science in the financial sector now extend to various sorts of Fraud detection, Risk management, Sales forecasting, Customer support, Asset Management, Service personalization, Product recommendations, Stock price prediction, and cybersecurity.
3. What is the role of AI/ML in improving cybersecurity?
Artificial Intelligence (AI) is finding its way into cybersecurity as its use grows more widespread. AI and ML backed automated detection and response to attacks, network efficiency, and vulnerability detection can be utilized to improve cybersecurity. AI systems can monitor millions of transactions in real-time.
4. What are the ways to leverage AI/ML models in fraud prevention?
Integrating AI/ML models in a cohesive strategy can help prevent frauds. It helps efficiently determine which transactions are most likely to be fraudulent, while significantly reducing false positives. The techniques are extremely effective in fraud detection and prevention, as they allow for the automated discovery of fraudulent and non-fraudulent patterns across large volumes of streaming transactions.
5. What is the future of fintech industry with AI/ML in place?
Technology such as AI and ML is revitalising the FinTech sector. Along with making day-to-day services run more smoothly, AI technology also helps companies approach unexpected issues with creative new solutions. As the demand for online banking services continues to increase, the adoption of AI and ML is becoming key to growth and sustainability in the fintech industry.