Data is no more eccentric to financial industry, neither is analytics or decision-making. When combined, the system can break through the data silos as well as turn the insights into rewarding business outcomes. Artificial Intelligence (AI), machine learning (ML), and predictive analytics are paving their way for intensive customer-centric data that can increase sales, generate leads, and enhance customer satisfaction. Overtime, the banking and fintech services has become one of the biggest consumers and producers of data.
Big data is shooting up as the companies get a move on to collect more insights about their users to enhance their product development. Predictive analytics mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends.
A study by New Vantage Partners shows that only 31% of companies have a “data-driven organization,” and only 28% have a “data culture.”
According to Gartner, predictive analytics describes any approach to data mining with four attributes:
- An emphasis on prediction (rather than description, classification or clustering).
- Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining).
- An emphasis on the business relevance of the resulting insights (no ivory tower analyses).
- An emphasis on ease of use, thus making the tools accessible to business users.
AI and its suite of technologies has several integral capabilities, making it a strong candidate for decision optimization:
- Rule-based approach
- Accurate pattern recognition
- Faster data processing speed
- Ability to anticipate future events
- Communication capability using chat and voice bots
How Does AI Impact Predictive Analytics?
Our decisions are usually not based on logic. It is affected by emotions, reviews, inner satisfaction, trust, intuition and culture that help us to make a particular decision or buy a certain product. AI algorithms can identify these key emotions and produce insights that make effective search for potential buyers, without which the unstructured data becomes highly complex to understand.
Predictive analysis paired with AI are going mainstream, helping businesses make quicker decisions and predictive outcomes. This amalgamation can be termed as Decision Intelligence. It helps in collection of data to make the right decision without settling for any sub-optimal option and further allows them to identify their potential customers or probable responses by using personalized data collected over time.
Cassie Kozyrkov, Chief Decision Scientist at Google, defines decision intelligence as the discipline of turning information into better actions at any scale.
Decision Intelligence in FinTech
FinTech specializes in the technological aspect of financial products and are predestined to develop and implement new technologies. They adopt a clear and goal-oriented approach. AI combined with predictive analysis in this domain is helpful in conversational customer service, anti-fraudulent systems, credit score management, data processing, claims and tax processing and other processes.
Decision intelligence can help with maintaining financial security with a great customer experience. It can also be implemented for automated due diligence for large transfers to speed up clearance. Mastercard’s Decision Intelligence solution collects a customer’s debit and credit card data in real time and then uses ML algorithms to analyze whether the transaction is authentic or not.
New Vantage Partner states that 92% of the respondents are increasing their pace of investment in big data and AI.
In Retail Banking, it provides several opportunities like data-driven product marketing, advanced customer segmentation and improvised pricing strategy. The factors affecting these decisions are usually based on customer demographic data, credit card statements, transactions, point of sales data and digital payments. It can even perform lead scoring tasks and optimize backend operations such as loan and credit card approvals. It also offers the ability to make personalized offerings and marketing strategies by analyzing customer’s interests.
In conclusion, the combination of data and AI or ML technology will allow banks and financial institutes to enlarge their customer segment, reduce costs, automate many processes and overcome various business obstacles.
Know more about Data Analytics in Depth.