Banking & Financial Services

Banks and financial firms are part of one of the most scrutinized industries in the world. Next Data Science combines the tools such as predictive modelling and machine learning which helps in spotting and preventing fraudulent activity, ease compliance and reporting, engage with & grow customer base and optimize marketing efforts.
At NDS we work on financial analytics which helps to understand the performance of an organisation, measure & manage the value of tangible and intangible assets of an organisation, manage the investment of the company, forecast the variation in the market, increase the functionalities of information systems and improve the business processes and profits.
We also work on predictive analytics by helping the banking sector in fraud detection, application screening, customer acquisition & retention, knowing customer buying habits, cross-selling, collections, better cash/ liquidity planning, marketing optimization, customer lifetime value(LTV) and feedback management.
We also cover risk analytics, service analytics, business analytics, big data analytics, credit card analytics, retail analytics and provides the consultancy for the enhancement of the business.

Case Study 1:

Problems are faced by customers and they are disappointed with the financial services of a bank like:
-> Waiting for hours in queues to transact with a teller.
-> Sitting with a bank representative who recently joined and has no idea about a particular customer
-> What his requirements are or what is the best way to serve?
Next Data Science try’s to develop bank’s intelligent system using machine learning to provide the bank representative with information related to:
-> All your accounts
-> Your balances
-> Your tenure of relationship with the bank
-> What banking products or services can the bank representative offer you?
Next Data Science’s achieves its success mantra “Know thy Client” that financial firms have sworn by forever and data science is the secret to achieve this success mantra.

Case Study 2:

Insurers around the world are struggling with a host of challenges:
-> Consumer trust levels
-> Competition
-> Regulations
-> Shrinking profitability and legacy IT issues.
-> They also generate massive amount of structured and unstructured data.
Next Data Science’s predictive and optimization models and artificial intelligence enables insurers to drive business decisions.
Insurers can then manage the entire lifecycle of a customer, from acquisition to lapse or claim and cross sale-up sale.
Data has been called the lifeblood of the financial sector. Next Data Science aims in bringing changes in the form of Claims Management, Customer Analytics, Fraud Detection, Health & Life Insurance & Risk Management.

Case Study 3:

Financial institutions have been investing heavily in data collection technologies, and have detailed transactional and consumer-level streams. The problem is collecting this data isn’t enough to provide business changing insights. How this data can be derived?
Next Data Science uses predictive and prescriptive analytics and machine learning algorithms to these data streams to extract insights and drive value for the customer.
NDS leads to those business changing insights that are derived both for customers with either small or large business.

Case Study 4:

Risk Estimation is the basic problem associated with customers in banking industry for unsecured loans.
-> Next Data Science’s Risk Analytics Team is seeking to increase “risk intelligence” by clearly defining, understanding, and managing their tolerance for and exposure to risk.
-> NDS Machine Learning & Artificial Intelligence Team’s enable clearer visibility into the challenges associated with managing the many types of risk associated with banking sectors.
By using NDS Advanced Analytics we derive certain measure, quantify, and predict risk. Now Banks can rely less on intuition and create a consistent methodology steeped in data-driven insights.