Introduction

Banks are exploring new frontiers in modernizing their systems using AI (Artificial Intelligence) . Beyond the obvious, robo-advisors, automated online financial and investment advisers, fraud detection systems, recommendation engines, voice recognition, conversational customer interfacing systems and cloud robotics are improving efficiencies in banks and financial firms. AI has impacted bank’s customer services, operations, risk and compliance, investment & trading, and cyber-security departments. In the area of wealth management, AI is being used to advise personalized, tax optimized investments to the investors. AI helps in improving straight through response, ease for use and cost reduction. The front office, middle office and back office operations are being targeted for process automation and AI plays an important role in the process automation. In the future, enterprise operational analytics can be scaled on demand to meet multiple business units. Enterprise Analytics fuel real-time operations to improve customer experience and provide best customer service to the high-net- worth clients.

Wealth Management

Emerging trends in Wealth Management like comprehensive financial planning helps integrating your business and personal needs into a plan to ensure you are able to meet your goals.The planning includes business succession issues, withdrawing money from the corporation tax- effectively and the taxation of the corporation at death and more. The other specialized services in wealth management include advanced tax and estate planning, managed investment programs, private banking, vacation home Planning, succession planning, charitable giving and real estate planning.

Wealth management firms are investing in digital capabilities which enable relationship manager and client interactions. Fiduciary duty is gaining prominence due to regulatory focus. There is likely to be a shift to a fee-based advisory model due to increased scrutiny and regulatory requirements. Firms will have to address existing Financial Industry Regulatory Authority (FINRA) and SEC rules while ensuring that the recommendations are in the best interest of the client. There are emerging adviser-client relationship models to cater to client demand. Evolution of new fee models, products and services for traditionally unprofitable customers are emerging in financial firms.

Community intelligence networks help in clients leading to better investment decisions. Intelligence networks bring innovation to get to market and scale faster. Innovation in brokerage services is enabling better investment and provide advanced decision support. There is an increased demand for data aggregation and analytics solutions which seamlessly sync data from disparate sources such as brokerages, custodians, financial institutions, etc. Standardization of customer experience across all channels is the current trend observed in banking and financial enterprises.

AI in Wealth Management

Wealth management firms are using AI to invest in a better way, evaluate the wealth market and gather customer specific behaviour. Artificial Intelligence is used to instantly identify available opportunities which deliver appropriate and relevant services and products to clients. The new AI applications introduce a number of business, security and privacy issues which will have to be addressed. Firms are investing in cyber-security to analyse the amount of sensitive data. AI systems could be optimised to ensure high-level blockchain security and crime detection measures across the data centres in the banking enterprise. Artificial intelligent services are used to identify and evade risks which might happen out of transactions made over the digital channel. Artificial intelligence could help managers make effective decisions for their clients through researching troves of data in providing the best results every time. Stock forecasting algorithms based on machine learning will support wealth managers by providing insights into the best strategies for optimizing and maximizing their portfolios.

Bank can use AI deep learning techniques to identify erroneous or incomplete data to avoid misleading decisions. Neural network, natural language processing, image recognition, speech recognition and sentimental analysis techniques are the deep learning techniques used in Banks and Financial Services. AI deep learning techniques are used to help with anti-money laundering programs, know-your-customer checks, sanctions list monitoring, billing fraud oversight and other general compliance functions.

Artificial intelligence can improve efficiency, weed out false-positive results, reduce costs and increase profits and make better use of workers’ time and company resources. NLP & Sentimental Analysis techniques help banks handle their compliance monitoring, automate some legal and regulatory work, handle most customer service and improve customer experience. Deep Learning & Neural Network Algorithms help in detection of fraud and creates a massive competitive advantage. They help in better identification and quantification of risk. Advanced pattern recognition by processing large data sets help in producing probabilistic predictive analytics to help forecast volatility and the downside risk to the portfolio. There is a surge in Robotic Process Automation in mid and back-offices of financial firms. Robo Advisors & Virtual assistants helps in replacing customer support functions who can move up to more value-added tasks. AI-based solutions can help wealth managers segment and micro-segment clients based on behavioral data. Alternative distribution, marketing channels for awareness, lead generation, end user- created investment solutions and natural language processing will help generate customized metrics and prepare reports on individual client portfolio data. Tailored advice, portfolios and product solutions are aligned to individual goals-based plans.There is a shift from technology- enabled human relationships to experiences with human support.

AI – Compliance

AI based compliance platform tracks users’ habits, activities and behavioural characteristics. Financial data and products can be personalized to meet and anticipate each user’s unique and changing needs. Each user will have one’s own digital personal financial assistant. IoT, AI, VR, AR and bots technologies are changing the way data is created, collected, interpreted, and communicated. Artificial intelligence can help banks handle their compliance monitoring by creating a natural language processing system to read through the legalities of regulations and reassemble the words into a set of computer-understandable rules. Each bank also needs to have a transparent system for total auditability so one can see who did what, and when. Platform users should identify erroneous or incomplete data to avoid misleading decisions.The new AI applications introduce a number of business, security and privacy issues which will have to be addressed. It will be important to ensure that these intelligent applications are developed in a way that they will provide the desired benefit and that the user can trust the advice and services provided. It will be important to be able to detect and isolate infected or malicious AI programs immediately and develop the effective policy and laws for governing their development and use, so that personal information is safeguarded and not misused.

Predictive Analytics

Predictive Analytics help financial services companies anticipate market shifts and customer needs. Data-driven management decisions at lower cost could lead to a new style of management, where future banking and insurance leaders will ask the right questions to digital assistant, rather than to human experts, which will analyze the data to come up with the recommended decisions that leaders and their subordinates will use and optimize their resources & funds for execution. By providing suggested outcomes in real time dashboards, the analytics have helped transaction bankers improve their decision making in the areas of risk, fraud mitigation, liquidity and collateral management.

Automated financial advisers and planners assist users in taking financial decisions. Intelligent knowledge assistants monitor events and stock and bond price trends against the user’s financial goals and personal portfolio and making recommendations regarding stocks and bonds to buy or sell.

What’s Next

Clearing House systems are using blockchain to manage the clearing and settlement of equity and other instrument based transactions. Blockchain is being used for client on- boarding, management of model portfolios, clearing and settlement of trades and compliance (KYC and AML). The distributed ledger based block chain helps in managing identity, control fraud, deliver services and regulation automation. Block chain helps in communicating portfolio changes to all clients subscribed to the model and provide individual account performance, drift outside of tolerances and cash flows. Biometric identity solutions and quantum computing might have high disruptive impact on blockchain adoption in banking and financial services. Voice assistants combine data analytics with natural language generation to produce a more human interaction.