Machine Learning (ML) and Artificial Intelligence (AI), the integral part of Corestrat’s Intelligent Decision Studio (IDS), are revolutionizing the world in enormous and rapid ways due to their ability to evaluate real-world problems with no or very little human participation. The banking sector is no different.
According to Business Insider, most banks (80%) are aware of the potential benefits of ML in decisioning workflows, and it is projected that they could save $447 billion by 2023 from AI and predictive and prescriptive analytics. Additionally, banks that pursue this opportunity will have access to larger, richer data sets to fuel Advanced Analytics (AA) and Machine Learning (ML), which can help them better serve their customers.
In order to compete in a market increasingly influenced by digital ecosystems, banks will have to build a comprehensive AI-and-analytics capability stack that consists of four main layers: reimagined engagement, AI-driven decision-making, core technology, data infrastructure, and cutting-edge operating models, which we shall discuss in detail in the coming sections of this article.
What is Decision Intelligence?
Decision intelligence is a data-driven approach to make better, faster decisions rather than being reliant on intuition or gut feeling. It uses AI, machine learning, contextual intelligence, and automation to generate actionable and specific business recommendations that can be immediately implemented to deliver commercial value.
Automating Decision Management For Digital Banks
Decision management for digital banking can be automated using rule-based systems, predictive modeling, and machine learning techniques. Rule-based systems involve creating a set of predefined rules that determine a course of action based on specific conditions.
For example, banks can automate the loan approval process using machine learning models to evaluate credit risk and make decisions based on pre-defined criteria such as the borrower’s age, employment status, income, and other required details.
Machine learning algorithms can be trained on past data to make decisions without being explicitly programmed. Banks can use these techniques to automate decisions related to fraud detection, loan approval, and other customer-facing workflows. Automating decision management aims to improve efficiency and accuracy while reducing the need for human intervention.
What is Intelligent Decision Studio (IDS)?
Corestrat’s IDS is a suite of ML-powered applications designed to help fintech firms and digital banks make data-backed business and/or customer decisions. Our experienced team believes in promoting a culture of strategy-driven problem-solving and innovation while helping you save costs and precious resources.
IDS: The Product Suite
Digital Lending Automation
Fintech, banks, and non-financial institutions can optimize risk-reward strategies for new client acquisition and portfolio management using our end-to-end, fully integrated lending decision platform. In addition, our automated digital lending workflow eliminates manual data rekeying and enables lightning-fast credit decisions.
Decision Management Suite
Our Decision Management Suite (DMS) provides clients with insights to automate and optimize lending decision-making processes using organization-specific ML-backed business rules. The DMS optimizes profitability by making better data-driven, risk-informed decisions using predictive models and deep learning.
Model.ai
Corestrat’s Model.ai provides model-building intelligence by automating the process of developing a predictive model without requiring you to write a single line of code. Model. ai creates predictive models using multiple ML techniques for the uploaded data and your target goals in seconds.
Decision Simulator
With Corestrat’s Decision Simulator, a reinforcement learning (RL) agent-powered platform, you can backtest multiple strategies and assess their impact on simulated portfolio profitability. This one-of-a-kind gaming-style portfolio simulation tool can generate unsupervised decision strategies to recommend optimal business outcomes.
Data Visualization
You can rely on our data visualization technology to convert raw data into usable information. Charts, graphs, and maps are available on our platform to help you see and understand trends, outliers, and patterns in your data. Our data visualization tool integrates with most BI systems and assists organizations in delivering KPIs and business metrics in a more engaging, interactive manner with more context and meaning.
Intelligent Decision Studio: Use Cases
Loan Origination
Intelligent Decision Studio (IDS) helps banks manage the entire lending process, from application to disbursement. Through IDS, banks can leverage our comprehensive loan origination platform to facilitate customer onboarding once the borrower has requested a loan. Additionally, IDS can help with multiple aspects of the loan application process, including checking credit history, loan pricing, digital KYC, and disbursal.
Credit Application Scoring
IDS-based credit scoring uses a lot of data, such as total income, credit history, transaction analysis, work experience, and other data points necessary for your business.
Our ML and AI-based algorithms use statistical models to analyze vast amounts of financial and non-financial data about borrowers. In addition, these algorithms are trained on historical data to identify patterns and relationships between variables that indicate creditworthiness.
The score is calculated using algorithms and statistical models that account for factors such as credit history, debt-to-income ratio, employment history, and other relevant information. The higher the score, the better the chance of the loan being approved and at a lower interest rate.
Credit scoring with IDS provides more sensitive, individualized credit score assessments based on a variety of additional real-time factors, allowing more loan applicants with diverse income or stressed credit histories to access loans. With nearly 2.5 billion unbanked people worldwide and less than half of the banked population considered creditworthy, the need for automated risk-informed credit-scoring solutions is obvious.
Customer Segmentation
Customer segmentation typically relies on massive data sets and, as such, must be designed appropriately. The goal of customer segmentation is to determine how to correlate product offers to customers in multiple segments in order to maximize customer benefits while minimizing credit risk exposure to the lender. In addition, our predictive analytics reveals specific insights that banks can use to understand their customers better and, more importantly, to increase profitability at the client level.
Customer segmentation by means of machine learning is a process of dividing a customer base into particular groups with similar characteristics. Using IDS, your customer segmentation becomes more accurate, dynamic, and ranked by risk and profitability potential.
Fraud detection
According to McCafee, fraudulent activity in the financial sector is a significant concern and has cost nearly $600 billion in the industry. With too much at stake, the banking sector needs to implement a tighter fraud detection process to minimize losses and better serve its customers. Corestrat’s IDS can help banks achieve greater fraud detection and loss mitigation.
IDS, which incorporates ML and AI, is the science of creating algorithms to detect fraudulent activities by automatically finding improvements based on previous experiences. It uses complex algorithms to identify patterns in large amounts of data.
Continuous and autonomous learning from this AI-enabled suite of products can assist in predicting and responding to situations even when they have not been explicitly programmed to do so, thus preventing fraudsters from deceiving banks.
Customer retention
According to a report by the Worldbank, 40% of adults in developing economies use digital channels for their financial needs and services, which allows banks to capture data regarding their preferences and experiences.
Banks can use IDS to create an in-depth view of each customer based on all available customer data, including account transactions, credit history, and spending patterns. Using this data, it is possible to provide customers with customized offers and micro-targeted, personalized recommendations via their preferred channels, thereby improving the customer experience.
Bottom Line
One of the key advantages of working with Corestrat is the level of personalization and customization we offer. Get in touch with us today to schedule a discovery call on how Corestrat can help boost profitability in your banking workflows while boosting productivity – at a fraction of the cost.
