Artificial Intelligence (AI) has transformed the physics of financial services. It has altered centers of gravity, isolated vulnerable bonds, and changed traits that are imperative to building a successful business. The ability to leverage data to drive process and talent optimization, product performance, and personalized interactions is now considered critical to driving value and efficiency.
By combining technology and robust data, AI delivers insight-driven decisions to resolve challenges. Financial services companies are becoming future-ready by harnessing the power of AI and Machine Learning (ML) to enhance operational efficiency, mitigate risk exposure, and customize product development, distribution, and customer experience.
According to PWC reports, AI will boost global GDP up to $15.7 trillion by 2030 and 45% of total economic gains will arise from AI-driven product enhancements that stimulate consumer demand.
Growth Potential of AI in Finance
Artificial Intelligence in the global financial technology market is expected to grow at 27.6% CAGR and reach $24.17 billion in 2026. The growth projection is attributed to its ability to increase security and efficiency, personalize customer experience, automate services, and unlock several other growth opportunities.
91% of financial services companies reported driving critical business outcomes by leveraging AI investments (Source: Nvidia Report). Here are the benefits they realized.
AI-led data analysis and reporting surpass human intelligence to cut costs, accelerate revenue, and optimize service quality and product efficiency. It creates operational efficiency, predicts customer preference, detects fraud, and plans market expansions. Although some industry voices expect AI to replace 9% of all jobs by 2030, fintech companies are optimistic that AI will expand their workforce by 19%.
How AI Transforms Banking & Fintech
Millennial and Gen Z customers prefer newer and faster technology over traditional systems. AI-based products and services enable FIs to meet the demands of today’s digital-age customers. It helps them enhance user experience through customized products and services.
Listed below are sample AI-enabled services that global banks use to enhance customer experience.
· Smile-to-pay facial scanning to initiate transaction
· Micro-expression analysis with virtual loan officers
· Conversational bots for basic servicing requests
· Humanoid robots in branches to serve customers
· Biometrics to authenticate and authorize
· Machine vision and natural language processing to scan and process documents
· Machine learning to identify risk patterns and cybersecurity incidents
· Real-time transaction analysis for risk monitoring
AI technologies augment the overall customer experience by offering personalized recommendations, insights, and suggestions. In short, AI powers banks, insurers, asset managers, and fintechs to outperform peers, boost customer lifetime value, and increase market share.
Top 6 Use Cases of AI in Fintech and Banking
Over 75% of businesses use high-performance computing (HPC), machine learning, and deep learning for AI applications. While retail banking and capital markets benefit from deep learning, fintech firms rely on machine learning for the use cases below.
- Transaction Fraud Detection
- Conversational Chatbots
- Robo-Advisors — Portfolio Optimization
- Regulatory Intelligence
- Credit Decisioning
- Algorithmic Trading
Transaction fraud detection
Financial firms leverage AI and ML to identify, investigate, and mitigate transaction fraud, money laundering, and KYC discrepancies efficiently. Analyzing large datasets and user behavior, AI can decode risk factors and help reduce chargebacks, fake accounts, spam, account takeovers, and any other CNP frauds in real-time. The fraud detection use case is critical in banking, card transactions, insurance, and lending platforms, to name a few.
Chatbots and virtual agents fall under the conversational AI use case. They are quite different from conventional chatbots. By leveraging Natural Language Processing (NLP) and Machine Learning (ML) processes, they can understand customer intent, troubleshoot issues, and make small talk based on context. In financial companies, they can be used to collect customer information, deliver support, resolve queries, and redirect escalations to customer service representatives. These chatbots can be used across multiple channels and can deliver support via audio, video, and text.
Robo-advisors — portfolio optimization
Building a strong financial portfolio is critical to maximizing returns on investments. Optimizing the portfolio helps balance the risk-to-reward ratio. AI-enabled Robo-advisors use predictive analysis to foresee future returns by analyzing the trends in the financial market. Investment companies can use this feature to make data-driven decisions that increase sales, optimize resource utilization, and improve operational capabilities. Robo-advisors are less priced options than conventional financial advisors. Even novices can drive value from investments with the help of Robo-advisors.
The banking and fintech sector must constantly update operations to stay compliant with governments and regulators. Using AI-led automation, compliance obligations are simplified, streamlined, and automated. The personalized, role-based dashboards provide regulatory intelligence for data-driven decisions at a lesser cost and time. Finance companies can stay up to date on compliance status and keep abreast with the latest regulations.
Credit decisioning engine
AI-based credit scoring can help expedite the assessment of creditworthiness and lending decisions. AI-led credit scoring models analyze real-time indicators such as current income, employment opportunities, and potential ability to earn, in addition to credit history, transaction patterns, work experience, and online behavior. This enables a sensitized and customized credit score assessment and inclusive lending decisions.
Trading is all about making profitable transaction decisions. AI-driven algorithmic trading uses pre-trade analytics and trading strategies to make lucrative transactions and groom efficient traders. Assimilating its own data using machine learning methods, AI-based algorithmic trading delivers real-time forecasts based on the analysis of price change patterns, currency values, global indices, raw materials, and other macro/micro indicators. It is super accurate in detecting market anomalies and helps reduce operational and trading risks.
Now, these are not all. Apart from the use cases discussed above, AI can also help automate and expedite underwriting, customer acquisition, and marketing optimization.
To know more about the use of AI in banking and fintech, write to us at email@example.com
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