Data Lakes 360°: Unleashing New Age Banking

Dive into the depths of data lakes to unlock the full potential of your organization's data assets

A data lake is a centralized repository for data flow from various systems. It enables easier access to data by improving data availability throughout the enterprise.

Data lakes facilitate collaboration among data scientists, analysts, and domain experts. They provide a shared platform where different stakeholders can access and contribute to the data and analytics process, fostering agility in model development and evaluation.

Banks can rely on data lakes to manage vast amounts of data, break down data silos and integrate fragmented systems built up through a traditional database architecture.

The Data Lake Market was valued at USD 3.74 billion in 2020 and is expected to reach USD 17.60 billion by 2026, at a CAGR of 29.9% over the forecast period 2021 – 2026.

What Exactly Have Data Lakes Transformed for Banks?

Traditional data warehousing technologies are based on relational database engines, data consistency, and thorough data structuring, but in the more recent period, the data lake has become more interesting.

The main advantages of the data lake landscape today are the use of commodity hardware, open-source technologies with cost-free software, and elastic scalability.

In the banking industry, data lakes can be associated with dealing with regulatory compliance requirements, fraud prevention, more accurately predicting financial trends, and improving and personalizing the customer experience.

By incorporating multiple enterprise data warehouses and data swamps into the data lake, banks can reconcile, settle, and regulate business using an integrated platform that not only reduces costs but also increases operational resiliency and productivity. It also reduces the headache of moving unstructured data from one place to another. Furthermore, Banks can store and analyze data streams to prevent security hassles.

Use Cases of Data Lakes in Banks

A data lake allows organizations to bring together diverse data sources relevant to default prediction, including customer data, financial transactions, credit histories, economic indicators, and external data feeds.

There are many applications of data lakes in Banks, but the following are the most common:

Customer Acquisition and Retention

Nowadays, banks make sure to use multiple data sources to get 360-degree insights on customers for constant improvisation. A well-managed data lake enables banks to pin down all data into a unified or integrated platform and gives marketers-controlled access to the data needed for customer targeting.

An intuitive visualization platform provides different views of the data and enables marketers to profile customers effectively.

Predictions in Collections and Recoveries

Data lakes provide a rich data source for training and developing advanced analytics models, including machine learning algorithms and predictive modeling techniques. By leveraging the comprehensive and diverse data stored in the data lake, organizations can build more accurate and robust models for default prediction and recovery targeting.

By combining and transforming variables within the data lake, organizations can extract meaningful features that capture relevant patterns and signals related to default risk.

Fraud Detection and Prevention

Transaction frauds and money laundering are always a hanging sword for banks and the main reason is that fraudsters can manipulate billing faster than banks can audit. A well-managed data lake can enable real-time data ingestion and automate fraud detection at an early nascent stage with predictive analytics that detects patterns of potential fraud.

Creating a centralized data repository (CDR) provides an intuitive user interface for search and ad-hoc analytics of all data and helps perform data analytics through an intuitive user interface.

Regulatory Reporting and Compliance

Banks face a lot of pressure in BASEL reporting in the context of data aggregation from RBI. RBI requires banks to o implement policies around data governance, data aggregation, and IT infrastructure, to ensure a timely, error-free, and 360-degree view of data across multiple lines of business to better understand, anticipate, manage, and mitigate risks.

A well-managed data lake assures automation for data aggregation to improve data quality and mitigates risks in real-time and generates risk reports to support changing internal needs and for auditing or supervisory queries.

D2k Technologies offers comprehensive data lake architecture for financial institutions, aiming to streamline banking operations. Contact us today!