How Machine Learning is Impacting Financial Lending Operations?

As many applications of machine learning are mainly focused on solving the challenges associated with profitability needs, competition with other firms, and demands of financial regulations. Integration of technology advancements always reveals several given potential benefits and risks for financial stability which is the need of the hour for the lending industry. Over the years, the growth in technology and the availability of more financial data, AI, and machine learning can contribute to a more efficient financial lending system.

The financial industry is rich with data when it comes to information like credit decisions of individual customers, financial markets, insurance contacts, and customer interactions. But not knowing how to use these hidden data resources to the optimum is keeping many traditional banks and finserv companies from developing at full capacity. Here are some of the potential ways in which AI and machine learning can be leveraged to churn data and get some value out of it.

• Financial institutions and lending vendors can leverage AI and machine learning techniques to harness customer data to extract insights on credit quality to price and market insurance contracts to automate client and customer interactions.
• Finserv and banking institutions can optimize scarce capital with AI and machine learning techniques to test and analyze the market trends, and capital gains on trading large positions, hedge funds, broker-dealers, and trade execution.
• Financial lending institutions can integrate this technology for scaling regulatory compliance, surveillance, data quality assessment, and fraud detection.

Importance of Machine Learning in attaining Financial Lending sustainability?

Financial industry eagerly looking to adopt AI solutions, it becomes important to understand where in your business AI can play a vital role to help with lending operations. Traveling back, many bankers have developed trust with FICO credit ranking systems.

However, with continuous innovation and evolution, lending rates and mortgage practices continue to change. FICO- which has worked very well in the past has stopped being scalable for future practices, has raised questions among bankers globally, what comes next?

The banking and lending industry is evolving at a rapid pace with the evolution of new technology innovations, changing consumer metrics, geopolitical dynamics, and unpredictable demographic trends.

Because of reasons like this, a new form of credit checking, banking, loans, and mortgages needed to be developed and practiced. And banks and lending institutions started to show interest in AI and ML applications. Machine learning and AI in lending and banking have brought new perspectives to fill in the limitations of FICO as a system.

Understanding the role of Machine learning in Financial Lending

While the lending and financial industry is eagerly looking to adopt AI solutions, it is imperative to analyze what AI or machine learning can offer to streamline business processes and how you can implement the same. Here below we have listed a few potential applications of machine learning and AI could provide.

• Customer-focused applications: credit scoring, insurance credit, & client-facing chatbots

AI and machine learning are already being applied to manage client portfolios, and contracts, and for pricing and selling insurance policies with algorithms. Credit scoring and monitoring tools are designed to speed up lending decisions, while potentially limiting the incremental risk. And similarly, the process of creating a customer/client portfolio can sometimes be an extensive endeavor, as each user has different goals and risks associated with them. AI and machine learning could streamline the process of developing a portfolio by assessing a customer’s goals and risk tolerance to develop an individualized portfolio by analyzing information like the age of a customer, their income, credit score, liabilities, and current assets — before spreading the customer’s assets across investments based on data predictions. 

Client-facing chatbots are virtual assistants built to help customers communicate and solve problems without human intervention. These automated applications use NLP to interact with clients in human language (by text or voice), and use machine learning algorithms to adapt, automate, and scale over time. 

• Conduct high-frequency trading

Adapting AI and machine learning to conduct high-frequency trading can be achieved by marking up sentiment indicators and identifying trading signals. AI and machine learning techniques to perform social media data analysis to provide sentiment indicators to a number of financial players to understand trading signals in real-time for effective decision-making to increase productivity and reduce costs.

• Detect Frauds And Threats to Financial Systems

Customer and financial data security and privacy can be at risk, as information is shared and acquired through digital platforms in various ways. However, AI and machine learning applications can ensure a greater level of protection by offering routine checks of risk factors that could affect customer privacy. Financial institutions can flag unusual behavior or misleading patterns in user flow using machine learning algorithms for monitoring and managing fraud. AI and machine learning can also replace passwords with personalized data like the recognition of a customer’s voice or face to advance system security and identify authorized individuals.

• Capital optimization

Capital optimization or the maximization of profits is the primary objective for any lending business to be given scarce capital. AI and machine learning tools built on the foundations of computing capabilities, big data, and mathematical concepts can increase the efficiency, accuracy, and speed of capital optimization using state of art strategies. For instance, machine learning has developed ways to conduct risk assessments to accurately predict credit scores for individual consumers, which could allow underserved consumers a chance to present themselves with credit profiles. Loan lenders for consumers, in return, can gain a competitive advantage over other bankers using traditional credit scores, as machine learning applications score and target consumers that have not been identified before.

While speaking of applications and the use case of AI and machine learning for the financial industry related to lending operations and optimizing the risk associated with it are numerous. However, for these technologies to be scaled at optimum there are a few concerns that businesses need to be aware of:

1. Mostly the accuracy of the model depends on the efficiency of information processing. For instance, credit decisions related to a customer can be affected by information associated with financial markets, insurance contracts, and customer interactions. To build an efficient financial system, the decision should be based on research and insights provided by every possible unit that is correlated.
2. At the same time, network effects and scalability of new technologies can vary in the given future due to the rise of third-party dependencies. This could in turn lead to the emergence of new technology or advanced architecture integration.
3. Applications of AI and machine learning could result in new and unexpected forms of interconnectedness between financial markets and banking institutions, for instance, based on the use by various Fiserv institutions of previously unrelated data sources.
4. The lack of complete knowledge or “audibility” of AI and machine learning methods could become a macro-level risk. 
5. As with any new product or service adoption, there are important challenges associated with appropriate risk management and oversight. It is vital for lending institutions to understand the role of AI and machine learning in view of their risks, including adherence to relevant protocols on data privacy and data security. Adequate testing and ‘training’ of machine learning tools with unbiased data and feedback mechanisms are important to ensure applications deliver what they are intended to do.

While data on the extent of adoption in various markets is quite limited, researchers suggest that some segments of the financial system are actively employing AI and machine learning. The use of Artificial Intelligence and machine learning technology is changing the way banking and lending institutions operate some financial services. In particular, fraud detection, capital optimization, portfolio management applications, etc. These applications are thus currently more widely used than other key FinTech innovations, such as distributed ledger technology or smart contracts and most market participants expect that AI and machine learning will be adopted further. 

To know more about AI practices in financial services or lending operations connect with SoulPage IT Solutions for a free consultation.