By - Sweta Sampath on November 2, 2022
The progress of artificial intelligence in legal industry for the purpose of digital lending is resulting in a rise in efficiency in the lending industry as A.I. continues to shape and accelerate how we manage information and process data. A recent example of how legal firms are combining document automation and AI lawyers like Kira for a case involving thousands of lending dispute-related claims for clients is now available. Legal documents are automatically drafted by AI lawyers to assist banks in adhering to new financial requirements.
The risk of inaccurate information and data inaccuracies rises as commercial banks and legal firms are under more pressure than ever to "churn out" contracts, loan agreements, and complex papers. The problems of preparing legal documentation are minimised and the rate of production is greatly boosted through the employment of artificial intelligence lawyers and automatic generation of contracts and agreements. Digital lenders increasingly utilise AI lawyers to assist them in resolving the issue for duties including credit risk assessment, loan application processing, debt collection and monitoring, customer support, and fraud protection.
As AI and machine learning grow more important, the financial sector is going through a technological transformation. Modern technologies advancements like AI lawyers have changed the traditionally drawn-out and time-consuming loan sanctioning process into a digitalized transaction that takes just a few hours. One of the most challenging processes in the conventional lending business is loan processing. However, in the world of digital lending, robotic process automation (RPA) fuelled by AI lawyers and ML may cut months-long operations down to just 10-15 minutes.
The automation procedure enables the businesses to extract pertinent data from the customer provided documents in order to validate all the particulars. Digital lenders are able to make more accurate and precise decisions owing to a data-driven strategy supported by machine learning. Customers must correct any incorrect entries in order for automated confirmation letters and intermediate bots to contribute to safer loan selections.
Customer Acquisition: AI can assist lenders in understanding this customer behaviour and forecasting potential business implications for lenders. This includes making assumptions about things like whether a consumer will actually buy a lending product. Using clickstream data, search data, and other similar data, AI models the customer's purchase intent in this case. Customers can be categorised into "must reach," "requires more effort," and "not interested" categories based on the results of the AI model. Lenders can target them and connect with them very early in the sales funnel based on this classification of clients and prospects.
Credit Scoring: AI may allow lenders to use an alternative credit mechanism. To calculate a customer's credit risk score, AI can analyse 300–400 data points on their behaviour, finances,income tax history, and other transactions. In the past, lenders have used financial and other data to determine credit. With AI, they can use more data points on client behaviour, which gives them a crucial competitive advantage. These organised and unstructured data can be ingested by AI models, which can then model the data and produce a credit score. With the use of this credit score, lenders can get in touch with current clients in an effort to sell them pre-approved loan products or with potential new clients.
Artificial intelligence (AI) and machine learning (ML) technologies are becoming more and more prevalent across all industries. The financial sector isn't far behind and has a lot of data. Utilizing these technologies, they created products that matched the changing requirements of their customers. By enabling more precise and quick decision-making analysis of consumer trends and patterns, machine learning has caused a stir in the lending industry.
As a result, Machine Learning (ML) is a branch of artificial intelligence that uses efficient algorithms and statistics to carry out particular tasks digitally and in real time by examining enormous data sets. Lending companies may quickly and concurrently identify, sort, and make precise decisions based on many data sources with the help of AI and ML.
Benefit of these technologies include:
Digital lending is developing into a lending giant with quicker turnaround times, improved speed, higher accuracy, and outstanding client support and service, coupled with a plethora of alternatives for plans, payment terms, and loan options. The significant barrier that traditional banks created has been overcome by digital lenders attributable to technological developments of all kinds, owing to ML and AI. The foundation of alternative credit scoring, which has made digital lending the industry powerhouse it is today, is made out of artificial intelligence and machine learning.
Digital lending platforms are heavily reliant on Artificial Intelligence (AI) which enhance data analysis and increase the effectiveness of credit risk assessment. Leading digital lending platforms utilise AI to analyse massive amounts of data to make data-backed lending choices, identify fraudulent applications, possible defaulters, and target good clients for cross-selling and up-selling of other products.
Yes, AI is used in Fintech. AI is utilised extensively in FinTech for a variety of tasks, including lending choices, client service, fraud detection, credit risk assessment, insurance, wealth management, and many other things. For increased productivity, improved precision levels, and quick query resolution, modern FinTech organisations use AI.
Benefits of AI in Banking -
1. Fraud detection and compliance with regulation
2. Enhanced Investment Analysis
3. Improved Client Experience
4. Lower Operational Costs and Risks
5. Improved loan and facility Evaluation