By Molly Cyrus 
Introduction
Similar to the financial world there has been a huge change in the recent past primarily caused by the integration of digital technologies. The integration of these technologies including AI ML big data blockchain and advanced analytics has altered the way in which the delivery of financial services takes place.
From robo advisors to virtual assistants fraud detection systems to automated loan processing these are being utilised as digital assistants in finance through which financial institutions businesses and consumers can reach heights. We trace the different roles of digital assistance in finance in this elaborate research which encompasses personal management of finance investment advisory services fraud detection and prevention regulatory compliance and customer service among many more.
In further detail we shall show the challenges and future trends of digital assistance in the financial sector. Finally we reflect upon how digital assistance is shaping the face of finance in this very elaborate research.
Emergence of E Financial Services
Financial Revolution
Financial services have always been the bedrock of economic development and prosperity. Over time the industry has transformed itself from very traditional and paperintensive procedures to rich digital platforms. Computers and the internet brought about a dimension for digital transformation in finance at the turn of the 20th century. This happened during the 21st century as the usage of AI big data and machine learning really boomed.
From retail banks to investment firms all the financial institutions started embracing digital tools to bring about improvement and better customer experiences and streamline processes. Digital tools have empowered financial institutions to deliver service customizations enhance security cut costs and give quick and precise answers to clients inquiries and transactions.
What Is Digital Assistance?
Digital finance assistance is the way AI enabled applications and technologies empower individuals and organisations to understand and keep their financial information in order. The four most common forms that exist today include virtual assistants also known as chatbots robo advisors fraud detection systems and auto customer service platforms.
At the core of digital assistance lies the fact that enormous amounts of data are processed in real time with a series of intelligent decisions that are based on that data. For instance a robo advisor goes about sifting through market trends assessing one’s risk tolerance and then making recommendations for investments. Similarly a fraud detection system scans transactions in realtime to recognize suspicious activities.
Digital Help in Personal Finance Management
According to all the fields where digital help has made a difference one area is personal finance management. Generally people don’t do too well when it comes to making budgets tracking their expenses saving money or making effective investment decisions. However digital help makes money management relatively easier through budgeting apps personal finance management platforms and AIpowered virtual financial assistants.
These apps can help keep track of all the expenses set budgets and monitor how much is being progressed towards the financial goals. Using AI and machine learning algorithms these apps would provide users with personally tailored insights into their spending habits and behaviour. For example they will be able to suggest which categories should be trimmed to cut back on spending and a more efficient way to save money while also generating alerts when overspent on specific categories.
Robo Advisors
Robo Advisors have transformed the investing landscape by bringing cheaper algorithm based financial planning services for investors. These robo advisors employ AI and machine learning algorithms in service to design and manage portfolios for these investors depending on specific goals risk tolerance and investment horizons.
Popular robo advisors include Betterment Wealthfront and Vanguard Personal Advisor Services. These companies have seen the highest adoption rates especially by millennials and other younger investors who prefer digital assistance over traditional access from a financial advisor.
Many benefits robo advisors have over traditional financial advisors include low fees accessibility and their availability around the clock. They can also provide personalised investment plans that change depending on the prevailing market condition and the changing condition of the person’s finances.
AI Based Virtual Financial Assistants
Also virtual financial assistants based on artificial intelligence such as Siri by Apple Alexa by Amazon and Google Assistant have been introduced for use in personal financial management. Such assistants can undertake much of any financial transaction that one might want checking balances paying bills transferring funds and even investment advice.
For example Bank of Americas virtual assistant Erica permits customers to get their account balances view recent transactions pay bills and view personalised insights about their spending. AI and NLP power these assistants making them easy and handsfree for users.
Digital Assistance in Business Finance
Corporate Finance Management
It involves payroll and expense tracking to ensure cash flow and forecasting for the business hence managing funds. The processes have been maximally streamlined with the use of digital tools which greatly makes them efficient and accurate.
This way platforms like QuickBooks Xero and Zoho Books offer cloud accounting that makes it easier for businesses to automate tedious tasks such as invoicing expense tracking and financial reporting. Such platforms are equipped with AI technology and machine learning to track financial data and even tell trends that will help indicate the financial standing of a company in real time.
Automated Loan Processing
The most tedious procedure that business finance engages in is loan processing. Traditional loan applications involve a lot of paperwork and long waiting times as well as extended verification procedures. All these are now automated by digital assistant tools that adapt most of the procedures involved.
Other fintech companies have established automated loan processing systems that make use of AI and machine learning when evaluating loan applications and even lending and granting loans in just a few minutes. Such platforms make lending decisions based on gigantic ranges of data points like financial statements cash flow and credit history.
Fraud Detection and Risk Management
With the advancements currently going on the biggest threat to businesses is fraud. As people are making more online transactions in contemporary times their numbers have been increasing exponentially and this threat of fraud has increased. Digital assisting tools have become an important requirement in the detection and prevention of real time fraud.
An AI based fraud detection system reviews transaction data combined with the behaviour of users to identify known patterns that may point to some form of fraudulent activity. Such systems employ machine learning algorithms which continually improve their accuracy and are very likely to detect even the most sophisticated fraud attempts. They often have more than enough time to take preventive measures that minimise the financial loss before it occurs.
Digital Support in Investment Management
Algorithmic Trading
Algorithmic trading commonly called algo trading is one of the biggest gains in investment management. In this type of trading trades are made using algorithms essentially a computer program designed to buy and sell given predefined criteria about stock price movements trading volume and many other market trends. These algorithms can look into huge amounts of data at multiple milliseconds providing them with an opportunity to execute trades at a speed that is out of human traders reach.
High Frequency trading or HFT is a style of algorithmic trading that has been around in the financial markets for quite some time. With HFT algorithms thousands of transactions are executed within a fraction of a second by exploiting price variability at very tiny levels in the market. Despite causing lots of criticism over contributing to market volatility HFT has had the effect of making markets even more liquid and thereby reducing the cost of trading.
AI Based Investment Research
Investment research is one of the most essential processes in making investment decisions. In the old days it was conducted by a financial analyst who employed manual methods in analysis through an assessment of financial statements market trends and economic data. Today these activities are fully automated through AI driven tools and investment research systems.
Kensho Alpha Sense and Thinknum are some of the platforms that use AI and natural language processing to analyse large amounts of data from financial reports news articles social media and market trends. These platforms can identify patterns and predict movement in the market. Investors can get actionable insights into investment actions through AI.
RoboAdvisors in Investment Management
Roboadvisors have also played a very important role in institutional investment management they enable the management of large scale portfolios with the aid of artificial intelligence and machine learning. It offers automated rebalancing tax loss harvesting and risk management.
Pension funds and hedge funds increasingly seek the advice of robo advisors for the best investment strategies. With robo advisors optimised investment processes can be achieved through automated investment reducing the expenses of human error and therefore improving overall portfolio performance.
Digital Support in Customer Service
Chatbots and Virtual Assistants in Banking
Customer care is the most crucial aspect of the finance sector and digital support resources have thoroughly changed the way and manner through which financial institutions relate with clients. AI based chatbots and virtual assistants are a norm in the banking world and have become practical tools that provide instant feedback to customers queries and instant resolution of issues.
For example Capital One’s Eno and the chatbot from Wells Fargo have been able to provide a long list of services to customers including checking account balances and transfers and even providing customers with certain types of financial advice. These sorts of chatbots employ natural language processing and interpret customer inquiries in real time to give accurate and personalised responses.
Customer Experience Enhancement
It has now become one of the main activities of making customer use experience very possible using digital assistance tools. Financial institutions can promptly respond to customer complaints and resolve the problem much quicker and more efficiently with the support they now offer 24/7. The AIpowered virtual assistant can now offer personalised recommendations based on a customer’s financial behaviour to make superior decisions based on their behaviour.
For example an end virtual assistant may derive the spending profile of a customer and suggest options to reduce expenses such as taking a low interest credit card or debt consolidation. This kind of personalised insight may enable financial institutions to deepen their relationship with customers and improve overall satisfaction in the long run.
Regulatory Compliance and Risk Management
Role of RegTech
A major challenge to any financial institution is that of regulatory complexity and fluidity. For your information the term regulatory technology is abbreviated as RegTech. It defines how the use of digital tools and AI assists a financial institution in better compliance with evolving regulations.
The power of AI and machine learning is leveraged by RegTech companies to interpret regulatory rules monitor transactions for suspicious activity and align financial institutions with AML and KYC regulations. This will then be made automatic thereby reducing the risk of noncompliance and also saving such financial institutions from fines and penalties.
Risk Management
Risk management is another area in which digital support has made a difference. Financial institutes face an array of risks such as market risk credit risk operational risk and regulatory risks. AIbased risk management platforms analyze enormous volumes of data to highlight potential risks providing real time insights for proactive measures by financial institutes.
For example machine learning can aid in processing data regarding trends in the market. From such trends possible economic downturns can thus be predicted financial institutions can make necessary alterations in their investments to avoid such downturns. Likewise algorithms by machine learning can find the creditworthiness of a borrower with all accuracy. Thus it reduces the risks of defaulting on debt.
Challenges of Digital Finance Assistant
In addition to these benefits however digital finance assistance does pose a number of challenges that need to be addressed.
Data Privacy and Security
Data privacy and security are the major concerns relative to digital support in financial services. Banking institutions handle a huge amount of sensitive information including personal and monetary information. The higher the levels of application of digital assistance in the financial sector the greater the exposure and the possibilities of attacks and data breaches.
Financial institutions must ensure robust security regarding customer data while guaranteeing that AIpowered systems are secure and not hacked or targeted by malicious activities. Further regulatory frameworks must be overhauled to meet the challenges resulting from digital technologies in finance.
Ethical Concerns
There are serious ethical issues related to the use of AI and machine learning in finance such as risks of bias in AI algorithms which may lead to unfair treatment for some people or groups. The other concern is the increased use of automation in finance service provision which is posing a threat to job security since most tasks that humans once conducted are now ascribed to machines.
In general financial institutions must ensure transparency and accountability of their AI systems and apply them in ways that are fair and ethical. More importantly attention needs to be paid to reskilling and upskilling employees to address the changing nature of work in the financial industry.
Regulatory Challenges
The technology rate of innovation in the financial industry far outpaces the regulatory development process. Thus financial regulators face the challenge of implementing regulatory measures on how digital assistance tools could be used in ways that keep fully intact existing regulations but at the same time promote innovation.
Regulators need to strike the delicate balance between innovation and the protection of a consumer against possible risk. This will necessarily lead to new regulations tailored to take into account new risks and the specific threats of new digital technologies in finance.
Future of Digital Assistance in Finance
The future of digital assistance in finance is very promising with technological developments in AI machine learning and big data. Among them are the following key trends
AI Driven Personalization
The more advances AI makes the more personalised financial institutions will be able to offer their customers based on specific needs and preferences.
Blockchain integration Blockchain can revolutionise finance digital assistants and ensure secure transparent and decentralised recording and management of financial transactions and records.
Increased Use of RoboAdvisors More comfort with digitally savvy financial services by the younger generations is expected to continue to push robo advisor adoption upward.
Enhanced Fraud Detection
AI powered fraud detection platforms will begin to become more sophisticated so financial institutions can better detect and prevent fraudulent transactions in real time.
Digital Financial Inclusion Platforms
The past couple of years have seen a great deal of change in the world of financial services. Through digitization speed accessibility and personalization most financial services have improved. One trend stands for strong financial inclusion through digital channels. Traditionally millions of people around the world have been underserved or completely excluded from formal banking systems particularly in developing countries.
Digital platforms many of which are mobilefirst have filled this gap by providing a host of services such as mobile wallets digital banking and microlending to previously unreachable populations. Companies such as MPesa in Africa have shown how digital finance can literally change lives through the use of mobile phones to provide simple financial tools.
AI for Improved Risk Management
This has shifted the integration of artificial intelligence in fraud prevention and credit scoring to redefine risk management. AI tools can assess vast amounts of data identify suspicious patterns in real time and make accurate risk assessments these are both for consumers and businesses. Such systems not only enhance security but also build user trust on digital platforms.
Blockchains Impact on Transparency and Efficiency
Another area is blockchain in finance. Blockchain technology provides more transparent transactions and secure transactions with fully decentralised ledger technology. Its applications are far beyond cryptocurrencies like Bitcoin areas involve trade finance cross border payments and contract management. Thus there will be fewer intermediaries as the cost will be reduced and the efficiency of transactions will increase.
Green Finance and Sustainable Investing
More importantly the green finance movement is now picking up speed as digital platforms are playing a very important role. Sustainable investing has reached out and democratised it through online platforms where individuals can invest in ESG funds. Digital assistance helps investors align their portfolios according to values so that financial growth is profitable and socially responsible. Innovation and inclusion in digital finance on a global scale are promised to come from the evolution of digital finance.
Conclusion
The financial industry has changed with the help of digital assistance in order to reap so many benefits not just for the individual but also for firms. From managing personal finances to advising people in making investments and fraud detection to regulatory compliance the process of digital assistance has made financial services accessible efficient and secure.
However digital assistance is a trend that is fast being embraced by the many and it at this moment brings along with it a myriad of challenges mainly relating to data privacy ethics and regulations. The financial industry is continuing to change and evolve. It is in this trajectory that financial institutions have to grapple with demands that arise as a result of digital technology.