The financial industry is a massive scene. In the US alone, the lending industry a has combined annual revenue of $36.5 Billion. This shows just how stakeholders in the industry should be in adapting new trends that will benefit them.

Technology has grown over the years, and the world is now moving towards Artificial Intelligence (AI) and Machine Learning. AI is becoming increasingly popular, especially in large organizations that handle huge amounts of data. Today, we look at how artificial intelligence is set to disrupt the lending industry.

But, first, what is AI?

AI and How It Works

Artificial Intelligence is the simulation of machines to think and act like humans. This is achieved by programming and using complex algorithms to mimic human-like traits such as learning and problem-solving.

Robots have often been portrayed as the benchmark for AI. However, the goals of AI are simply learning, analyzing, and reasoning. AI keeps evolving and outgrowing its previous forms. For example, a calculator is now just perceived as a normal computer function. This is because AI has moved on to much more complex functions and tasks.

There are primarily two forms of AI:

The finance industry has been enjoying the fruits of embracing AI for a while. From chatbots and machine learning to personal assistants, financial organizations have been investing in peripherals of AI.

Now that we've gotten that behind us, let's look at how AI is transforming the lending industry.

1. Credit Decisions

Did you know that a majority of buyers prefer using credit cards instead of paying by cash? This shows that credit is king. However, people don't prefer credit just for the payment options. Many choose to have credit cards to improve credit history.

Credit history is nowadays critical. Activities such as job searching and even renting an apartment are now attached to credit history. This means that making credit decisions is more important than ever.

Banks and lenders that have embraced Artificial Intelligence can make smarter credit decisions by accurately assessing a borrower. AI uses various factors that lead to smarter and data-based decisions. AI has its own credit scoring mechanisms that use more complex algorithms compared to traditional systems.

This is mainly beneficial to people who are viewed as undeserving of the traditional system. Lenders can distinguish applicants who present a high risk and those deserving but who lack a strong credit history.

Another benefit of AI in making credit decisions is objectivity. When using traditional credit scoring systems, lenders can be biased with friendly applicants. A machine isn't likely to take sides, unlike humans.

Digital banks and online lending companies using AI get alternative data, such as smartphone data, and use it to evaluate creditworthiness and offer personalized loan products.

2. Managing Risks

For a long time, lenders have used credit scores to divide borrowers in terms of those deserving and those who don't. However, this method hasn't always been efficient for the business. A credit score can't entirely determine who will repay the loan in full in the agreed terms.

As the old adage goes, time is money. However, risk can be fatal if not managed properly. Collecting data and analyzing it accurately can go a long way in cushioning many businesses. How does AI help in risk management?

One of the features of AI is herculean processing power. This allows copious amounts of data to be collected and processed within a short time. AI also helps manage both structured and unstructured data, which would be a difficult task for humans. Algorithms make more accurate decisions using various variables.

Data that can be collected and analyzed include how many active loans a borrower has, how many have been repaid in full, how long they take to repay their loans and how many credit cards they have. All this data can be used to make lending decisions that make more business sense. Only a machine would be able to go through all this information and accurately give a solution.

This allows risk managers in financial institutions to identify risks early and effectively, thus prepare to cushion themselves beforehand and make better decisions.

3. Personalization

The traditional methods of offering financial services are getting left out in today's digital world. This is where AI takes the cake when exploring new ways to provide tailor-made services to the customers.

One such way that AI provides personalization in the lending industry is by the use of chatbots. AI-powered chatbots help borrowers find self-help solutions without having to contact the call-centers.

While it might be perceived that lack of human interaction might result in customers' loss, the inverse is true. Clients become more loyal to a brand that offers more personalized services. When customers are loyal, they are more likely to purchase your products and services and refer you to their friends and family. Lenders that have personalized their services have higher client retention rates.

Take, for example, a lender that has used AI tools to offer personalized financial advice. More clients will be attracted to this kind of service because being associated with a financial institution helps them hit their financial targets and goals. Such tools track your income, expenditure, and spending habits to offer you a custom financial plan.

Big financial institutions have already developed and launched apps that help their customers pay their bills on time, plan how they spend their money, and allow them to easily interact with their bank.

4. Cybersecurity

A lot of sensitive information changes hands on a daily basis during financial transactions. Digital transactions nowadays allow people to make payments, deposit money into their bank accounts, trade-in stock, and forex via online accounts and smartphone apps. What would happen if malicious individuals got access to these transactions?

This has necessitated the need to enhance cybersecurity. AI has been successful in the fight against financial fraud for several years. It has especially been successful in preventing credit card fraud. Credit card fraud cases have risen over the years due to the advent of e-commerce and online shopping.

AI algorithms study your online behavior and habits, location, and buying tendencies. In case any irregularity is detected, a security mechanism is triggered. An irregularity may include behavior that is out of order with the studied pattern.

AI is used to combat the following cyberattacks:

• Internal threats- Sometimes, the threat can come from a malicious insider. They might get involved in activities such as theft of money and money laundering. AI tackles insider threats by monitoring logs from all user devices and accounts. Machine learning further uses behavior analytic tools to analyze each user's behavior. Any change in this behavior is pointed out as an anomaly. This includes when an employee tries to access an unauthorized account or information. Even when the employee has the necessary credentials, AI will still detect a change in behavior and alert the systems administrator.
• DDOS attacks- Distributed denial of service (DDOS) attacks occur when hackers place malicious requests to deny access or services to legitimate users. This can tarnish the name of a reputable company. Hackers mainly use this form of cybercrime to blackmail and extort money from financial institutions. AI monitors a bank's network and can detect any illicit requests and block an attempted DDOS attack.
• Phishing- Phishing is nowadays one of the most prevalent cybercrimes. It takes place when a malicious individual contacts people via email, phone, or text message, compelling them to give up sensitive information. The information involved often includes bank information, credit card details, or passwords. Phishing scams can sometimes not be identified because of their similarity to legitimate sources. AI can identify phishing emails. Further, algorithms can flag and blacklist illegitimate sources and infected files.

AI improves the efficiency of fighting cybercrimes. One amazing feature is that algorithms don't have any reservations when it comes to learning. If a human corrects it when it raises a red flag on any activity or transaction, it can learn from that and make more complex decisions.

5. Automation

Robotic process automation can help the lending industry cut costs while boosting employee productivity. Also, a lending company can maximize profits from each loan by reducing or eliminating administrative overhead.

For many years, banks and lending institutions only used computers to automate some loan application processes and approval. But now, AI is helping them fully streamline the process.

As you may have already noticed, online loan application processes and approval barely take more than a day or two. This is an improvement from the traditional lending system where you had to file paperwork with your personal details and reason for loan application. After that, you had to visit the banking hall to drop your application in physically. Approval for your application was not assured, and you had to call your loan officer or keep visiting their office to know the status of your application. This whole process would sometimes run for a few weeks.

With AI, the lender can quickly analyze your creditworthiness and background, which gives accurate results. Human analysts can make grave errors which hugely cost the lender. AI collects and analyzes data over time, which reduces the chances of errors even while handling enormous amounts.

In addition, lenders can monitor loans more efficiently. AI can help loan companies pinpoint the loans that are likely to be defaulted on and take precautionary measures. This could possibly disrupt debt collection activities, which are stressful.

Other than personalization, chatbots help lenders save a lot of time and cash by streamlining processes in the call center.

Automating processes using AI not only streamlines tasks that are monotonous when done by humans but also significantly reduces errors and lets the company's management refocus the workforce into activities that require human efforts.

What Should You Expect from AI in the Lending Industry?

When it comes to predictions for AI in lending, one thing is for sure: AI is changing the financial industry's face.

Many people predict that AI will add more security features for transactions and accounts. This is especially as we expect the adoption of cryptocurrency and blockchain. This might also see the reduction or complete elimination of transaction fees because there won't be any middleman.

Managing personal finances is also set to be made way easier by digital assistants and smartphone apps. This is because machines will have the ability to plan and perform tasks by themselves. Such tasks include paying bills and making tax filings.

Customer care will also improve with complex Virtual Reality (VR) systems. Transparency will also be enhanced by in-depth and accurate reporting and better history checks.

Bottom Line

AI is definitely the future of the lending industry. The speed at which players in the industry are embracing the technology to streamline their operations will see them provide quick and efficient solutions. AI is vital in making credit decisions, by distinguishing borrowers who present a high risk, and those who might not qualify but are creditworthy.

AI also helps companies manage risks efficiently by collecting and analyzing large amounts of data on trends. Companies can then use this data to prepare for any changes or shifts in the market. Further, AI helps curb the chances of fraud and cybercrimes by triggering a security mechanism whenever an anomaly in accounts or transactions is detected.

AI will take the lending industry by storm and lead to the streamlining of processes and a better customer experience.