How Digital Platform with Advanced Artificial Intelligence Technology are Customizing User Experience
The combination of AI technologies such as machine learning and NLP along with data science and analytics have emerged as important tools for companies to streamline their personalisation activities such as in digital marketing and recommendation engines.
In the not so distant past, a company could easily gain the loyalty of its customers by simply selling superior products or services. However, as the market saw a deluge of numerous similar products and services with little to set them apart in terms of value, companies had to constantly be looking for innovative ways to acquire and retain customers. As brands realised the role of customer or user experience (UX) in a person’s lifecycle, they began to seek ways in which to deliver additional unique value across all stages.
As consumers started interacting and transacting with brands through online or digital channels, companies leveraged this trend by giving customers the option of personalising products and services to meet their requirements. Today, most companies across various industries such as e-commerce, travel, education, etc. will concur on the importance of personalisation in winning customers and building brand loyalty among them. And truly, personalisation has become an important tool and a key element of any brand’s value proposition, which if delivered effectively, can bring it great success. The effectiveness of the tool is particularly notable on digital or mobile platforms, thanks to AI techniques such as machine learning and natural language processing which are enabling brands to use customer insights in ways one could never imagine before.
How machine learning and automation helps personalise the customer experience and journey
In the financial services industry too, the use of Artificial intelligence has evolved considerably over the past couple of years thanks to the increasing popularity and adoption of digital platforms and the push towards digitisation, presenting a significant opportunity for the service providers to reach out to consumers directly over online channels. Also, such channels are a rich source of data that can be leveraged to deliver greater personalisation to customers and ensure higher levels of satisfaction. The combination of AI technologies such as machine learning and NLP along with data science and analytics have emerged as important tools for companies to streamline their personalisation activities such as in digital marketing and recommendation engines. More than anything, big data and AI allows companies to make sense of large volumes of data from disparate sources and apply algorithms on such data sets to derive useful and unique insights on customers, find out about their specific needs and wants, their propensity to continue using their services, and loyalty to their brand. This allows companies to tailor their offerings to deliver additional value as compared to their competitors. Some of the major areas where artificial intelligence has had the most impact is the creation of chatbots (Natural Language Processing) and development of new-age credit scoring models (machine learning). Let’s look how.
Credit scoring models
Credit scoring models employ a host of supervised machine learning algorithms to accelerate lending decisions and make them more data-driven, while potentially limiting future risk. Customer’s historical financial transaction and payment history data from financial institutions serve as the foundation of most credit scoring models which use tools such as regression, decision trees, and statistical analysis to generate a credit score using limited amounts of structured data.
Alternative credit scoring models, on the other hand, use a range of one-dimensional and multidimensional data available in the digital footprints left by users on websites like Facebook, Twitter, and LinkedIn. The information provided by these data sources goes beyond the usual financial and credit history of EMIs and credit card payments, and instead looks at different aspects of an individual’s personality to determine their creditworthiness.
Some of the commonly used alternate date techniques leverage upon bank account history of customer. The bank statements are used as source to analyze spending patterns, constant payment patterns of existing EMIs, average balance maintained etc. Another alternative to estimate customer income could be tie-ups with telecom companies to leverage data on customers like number of instances a prepaid customer’s mobile balance was zero, time taken to recharge the phone once the balance was zero on prepaid customer and parameters like international calls, roaming, call history and consistency of balance.
The real challenge lies in mining the available data effectively and making meaningful and sensible correlations using algorithms and analysis. Sensible and accurate correlations can help predict a person’s credit behaviour and introduce potentially underserved customers to the formal credit market.
Using traditional and alternate sources of data available, financial institutions perform feature engineering on the dataset; identifying statistically significant variables which enables scoring models to differentiate between good and bad borrowers.
The key going forward will be track the effectiveness of these credit scoring models. While lending based on these models is happening big time and it is bringing in efficiency, effectiveness and credibility of the same is unknown and largely untested.
Product Recommendation System
Financial institutions offer various services to customers ranging from different products like fixed deposits, credit cards, loans, insurance etc. The objective of recommendation systems is to find out the merchants/items which a customer might be interested in after already availing certain services from bank. Financial institutions have started taking a much broader view on customers beyond one transaction and look at metric like Customer Lifetime Profitability Value. Customer lifetime value (or CLV) is the worth of a customer over the length of their entire tenure. Using customer lifetime value as a metric requires your entire bank to shift its focus from quarterly profits to the long-term health of customer relationships.
CLV also provides an upper limit on customer acquisition costs and provides insights into customer engagement by making informed decisions on product & pricing. It also differentiates between customers which are worth pursuing for long-term value and which ones are likely to switch over for balance transfer. Understanding the difference between different types of customers guides enables these firms to maximize their value and cater to different segments appropriately. It estimates the profit & loss if many competing items can be recommended to the customer. Now based on the profile of the customer, recommends a customer centric or product centric offering. High value customers, which other institutions are also interested to gain wallet share, are usually provided best of your offers to improve customer loyalty. Recommendation system are used to finds out the products which a customer might be interested into after buying something else. It estimates the profit & loss if many competing products can be recommended to the customer.
Risk Management & Fraud Detection
Identifying individuals who have a high risk for fraud is a task inherently suitable for machine learning. These machine learning solutions can comb through huge transactional datasets and identify all cases that might be prone to fraud. While providing a loan to any client; financial institutions go through a process of risk assessment to estimate the creditworthiness of a prospect. Traditional systems relied on historical data like transaction history, credit history and income growth over years to understand the risk associated with every loan extended. This resulted in inconsistent estimates as historical data is not always an accurate standard to predict future behaviour.
With the help of machine learning, fraud detection techniques can be made efficient and effective. The solutions created can analyze historical transaction data to build a model that can detect fraudulent patterns. Companies are also using machine learning for biometric authentication by collecting data from various sources and then mapping them to trigger points, AI solutions can find out the rate of defaulting or fraudulence for each potential customer thus alerting the financial institutions beforehand that giving any credit to these individuals is risky.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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