Thinking beyond chatbots, how effectively do BFSI firms utilise AI and data?
AI and data in the BFSI industry are filled with untapped potential, many view AI only as chatbots, but it can be much more.
Building AI-powered decisioning capabilities enriched with internal and external data and augmented by edge technologies is the first significant step for BFSI companies to generate more ROI and business growth.
The increasing demand for analysing and deriving meaningful insights for banking processes will support market growth, and the AI-based solutions help make better decisions and increase ROI. Moreover, the exponential demand for data analytics is attributed to the growing digital data and the pivot towards a customer-centric business model.
AI in the BFSI market, from data analytics to the visualisation solution segment, is expected to grow at a 30 per cent CAGR from 2022 to 2028. More accurately, it is expected to reach $68.23 billion in 2028, according to Emergen’s research analysis.
While the major players offering AI and data analysis solutions for the industry are Amazon Web Services, Google, Intel, Microsoft, and Oracle, several others are working towards innovative and effective solutions.
Temenos, the cloud banking platform’s explainable AI (XAI) technology, offers innovative solutions for transparency to customers and regulators on how AI-based decisions are made. One of the first platforms to bring transparency and explainability of AI automated decision-making to the banking industry, Temenos recently announced that the XAI would be offered on Oracle Cloud Infrastructure (OCI) to allow customers to deploy XAI and ML capabilities through Oracle Cloud Marketplace.
Hani Hagras, Chief Science Officer at Temenos, said: “With increasing reliance on AI and machine learning technology to automate decision-making, there is a real need and opportunity for businesses to adopt true explainable AI models.”
Zest AI is a financial software company that develops and supplies ML software to evaluate borrowers’ risk. The Zest model management systems increase approval rates and minimise risks related to faulty credit decisions by analysing extensive credit data. Last year, the company raised $18 million for further development.
And more recently, Bud Financial, a provider of an AI-based open banking platform that helps power lending and other personalised products, raised $80 million.
Active.AI serves BFSI customers across 43 countries with conversational banking as a service platform. Recently, Gupshup, a conversational engagement provider, acquired Active.Ai to strengthen CX solutions for BFSI customers.
Moreover, according to Autonomous Next research, the average cost savings for banks from AI applications is estimated at $447 billion by 2023.
Exploring some use cases
According to a PwC-FICCI report, enhancing customer experience is a top reason for implementing AI.
The banking industry has been tackling inefficient management and operations for a long time. Unnecessary manual work due to the siloed management makes businesses focus their AI efforts on connecting fragmented data.
Integrating and using structured and unstructured data in real-time is challenging for banks. Customers with different needs are sometimes offered the same products.
As noted by McKinsey in an AI report, “analysis from internal and external sources at scale for millions of customers, in (near) real-time, at the point of decision across the organisation,” can help banks offer efficient and personalised customer experience.
Rapidly evolving CX and real-time data processing make personalisation critical for the BFSI industry. Driven by behavioural and data science to develop real-time insights, banks can make informed decisions based on a customer’s purchase history, each with something that seems personally relevant.
For instance, banks can set up a data infrastructure with three components: input, platform, and sharing, as it is imperative to generate and capture new data frequently. This could include partnering with third parties. By integrating all the data, banks will develop algorithms to identify behavioural patterns, model customers’ propensity to buy a product, and offer timely products and services.
Transaction monitoring has been another customer pain point for a long time for full-service banks. AI solves this problem by helping banks shift from rule-based analysis to risk-based assessments. RPA solutions can populate regulatory reporting formats with existing data and archive reports electronically.
Natural language generation (NLG) advancements allow relevant information about detected cases to be gathered into a coherent narrative for analysts, supporting assisted creation of reports.
Meanwhile, due to the dramatically increased number of transactions that banks process today, it’s effortless for fraud to find loopholes and enter the system. And threat actors are using sophisticated tools to bypass traditional defences built to detect threats. AI holds another use case here. It can understand online traffic patterns and determine device fingerprint validity to customers over a period of time.
Moreover, ML and predictive analysis can help predict fraudulent transactions and identity thefts, improve KYC automation, and detect unusual patterns in individual usage.
A look into the ME market
Over 50 per cent of the Middle East population currently do banking on their mobile phones. As a result, banks are on edge to drive their CX with AI-enabled automation.
According to IDC, almost 85 per cent of banks implemented AI applications to enable intelligent decisions and automated processes for corporate know-your-customer (KYC) procedures, reducing the time it takes to approve enrolments for new corporate accounts.
An Automation Anywhere report predicts that Intelligent Automation could increase Saudi Arabia’s economy up to $1.6 trillion by 2030. Although banks try to include AI into banking for better CX, experts recommend they also excel at Business Process Automation (BPA).
Last year, Qatar Islamic Bank (QIB) launched a conversational virtual assistant, Zaki, armed with proprietary Al and ML algorithms. It was designed to provide contextual responses to customer queries. Customers can initiate transactions through voice or chat, providing a secure channel for banking with QIB.
A 2021 McKinsey report states that banks that aim to be global competitors need a holistic AI and data analytics stack comprising four main layers: reimagined engagement, AI-powered decision making, core technology, data infrastructure, and a leading-edge operating model. And sure enough, the AI and data-led banking future is here.
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