AI in finance: Is Traditional finance dead?

The rise of new technologies has ushered in an era of improved connectivity, cloud computing, artificial intelligence (AI) and blockchain solutions. Financial Technology (Fintech) firms are capitalizing on this to disrupt existing, inefficient operational structures (modus-operandi) of these Traditional banks, with many seeing this trend as the way of banking, thus efficiently allocating scarce resources from surplus units to deficit units (lending), providing speedy and cost-effective payments services, cheap money transfer and account maintenance. These disruptions have brought a bad narrative on traditional banks on how they operate inefficiently despite their profitability.

AI in finance is the application of AI techniques to finance.

Some of the disruptions are in the field of payments and remittance, customer identification and verification, fraud prevention, supply chain and resource allocation.

What is Traditional finance

Traditional finance refers to financial institutions that maintain old ways of delivering of financial services such as lending, overdrafts and account opening in 'brick and mortar' structures that most at times require the physical presence of customers.

What Is Financial Technology – Fintech

Financial technology is the technology and innovation that aims to compete with traditional financial methods, they achieve this by automating and improving the way existing financial services are delivered.

Fintech firms may be startups or established financial institutions enhancing the usage of financial products through innovative and convenient delivery. The use of computers, smartphones for mobile banking, investing, borrowing services, and cryptocurrency transactions regardless of location

Traditional Banking /Centralized finance

Traditional banking refers to the ways in which financial institutions adapts in delivering financial services while centralized finance connotes the governance structure used by financial institutions to dictate the rules of business and enforcements.

What is Decentralized finance - DeFi

Decentralized finance (DeFi) offers innovative financial instruments to users without relying on intermediaries such as brokerages, exchanges, or banks. They achieving this by adopting blockchain technology (with smart contracts dictating the governance). DeFi introduces avenues where people can lend or borrow funds from others (liquidity providers), speculate on price movements, trade cryptocurrencies, arbitrage, insure against risks, and earn interest in savings-like accounts at speed and cost-effective ways.

AI vs Machine learning

Artificial intelligence and machine learning are closely knit and connected. Because of this relationship, when you look into AI vs. machine learning, you’re really looking into their interconnection.

AI describes a concept where machine are designed to mimic cognitive functions that are associated with humans, it includes learning and problem solving. It is done by programming rules to tell machines how to behave certain circumstances.
Machine learning on the other hand is a subset of AI, It's a process of applying mathematical models to assist machines or computers to work without being expressly instructed.

Perceived challenges of traditional finance

Challenge #1. Increasing FinTech competition : Just as it exists in other industries, digital disruption is pervasive in banking. With the enabling of access to banking services over the internet, traditional banks are no longer just competing with traditional local competitors. New FinTech business are on the rise, offering cheaper alternatives to financial services with near zero fees. Some of these firms include, PayPal, Square, Wealthfront and others.

Challenge #2. Slow processes : Most traditional banks are noted for slow processes in customer transaction executions. These slow processes are usually caused by its bureaucratic internal hierarchy or sometimes the culture of the lack of a sense of urgency.

Challenge #3. Rising Expectations : Today’s consumer is smarter, savvier, and more informed than ever before and expects a high degree of personalization and convenience out of their banking experience. Changing customer demographics play a major role in these heightened expectations: With each new generation of banking customers comes a more innate understanding of technology and, as a result, an increased expectation of digitized experiences.

Challenge #4. High operational costs : In general, traditional banking commissions are higher due to their high operating expenses, which makes many of their products or services more expensive compared to other products or services of fintech companies.

5 ways AI can revolutionize banking

Fraud Detection : To help fight fraud, specially designed AI algorithms that can detect anomalies or unusual patterns can be used effectively. Detecting subtle distinctions that often go unnoticed by humans is where AI algorithms thrive effectively and at scale.
Fraud is a financial crime that includes credit card fraud, false insurance claims, loan application fraud and organised crime, to name a few.

Credit decisions : As the need for credit applications are continuously on the rise, calls for business processes approval automation has also become more critical than ever. Credit decisions have traditionally been evaluated by humans using a scoring method that considers the borrower's previous performance, some also resort to the use of credit bureaus. Most at times, these decisions a fraught with personal bias, as well as a lengthy manual process.
This is where AI comes in, AI assists lenders to make more accurate and faster decisions in resource allocations. Aside the faster process automation, AI can also objectively predict potential borrowers financial health at a lower costs and free from bias. Thus enabling firms to make informed decisions about their customers and differentiate high-risk from low risk creditworthy individuals.

Risk management : Risk management is a crucial area of focus for financial institutions, the goal of risk management is to keep organizations safe from potential threats that might negatively impact a financial institution's going concern and profitability. The threats are numerous, however, they are mostly include, but not limited to, credit risk, political risk, legal risk, market risk, inflation risk.
AI model capable of analyzing big data (huge volumes of data) to identify market trends, prioritization of risks, and risk monitoring can be used in this regard.. This enables effective, efficient and timely management of risks of enterprise or business related risks.

Personalized services : Enhanced personal services is what the average consumer demands from financial services. It is only in the use of AI that financial service firms can acquire the relevant and applicable data to provide the personalised services and information based on individual behaviour. Meeting this demand may come at a cost, but it is may prove rewarding in the longrun.

Algorithmic Trading :The human brain is very limited in the amount of information it can analyze in a single moment compared to machine learning algorithms. Analyzing thousands of fast data from historical and market trends is technically an inefficient way of making any real gains due to the volume and time bound information needed. Using AI, one can evaluate both expected gain and potential risk and return, can also reduce the likelihood of mistakes in making trading decisions.

What happens if Traditional banking does not innovate?

The probability of extinction and reduced profitability will be inevitable when traditional finance firms fail to innovate. With reduced profitability, firms will have to close down, layoff workers and charge high prices to cover their operational costs.

As a result of the financial intermediation role played by these banks, at any point in time, one bank is likely to be indebted to the other, and they will fail to settle their liabilities with the associated interest. Eventually, it could become systemic.

Blockbuster, Nokia and others failed to innovate in time, as a result, couldn't catch up with the internet of things and became extinct.

Conclusion

The need and benefits of emerging technology can not be downplayed, the game is changing, players need to take advantage of these demographics trends and technological innovations in order to stay relevant to the market.