Machine learning applications enable to the
Bank to predict customer needs and determine in real time which offers to give
each customer. For example, staff can deliver real-time, localised, and
personalised interactions to each customer at the right time, with the right
content, and using the right channel.
Financial service institutions globally are
striving to deliver consumer-centric digital services demanded by a new
generation of tech-savvy customers. These new services represent a massive
opportunity as well as a massive risk as more consumers connect to products and
services digitally. To capture the opportunities, while mitigating the risks,
the institutions need the ability to gain a single enterprise view of customer
Danamon, one of Indonesia’s largest financial institutions, also wanted to
obtain a holistic view of customer behaviour across the bank.
The Bank offers corporate and small
business banking, consumer banking, trade finance, cash management, treasury
and capital markets services. Each line of business in the Bank has their own
data mart, resulting in mutliple data silos. So, a platform was required to
integrate data from multiple systems.
Bank Danamon adopted a machine learning
platform powered by Cloudera for
real-time customer marketing, fraud detection, and anti-money laundering (AML)
The platform pulls in data from about 50
different systems. More than one terabyte (TB) of unstructured and structured
data is ingested and analysed daily, both in batch mode and via live streaming.
The data includes transactional, product, internet banking, mobile banking,
credit card, customer care, voice, digital log, social media, social economic,
and other third-party and external data.
As it implemented a modern data platform,
Bank Danamon wanted a full range of analytic capabilities, from descriptive to
prescriptive. It used the Kogentix
Automated Machine Learning Platform (AMP) to help it effectively create
the advanced machine learning models needed to improve business outcomes.
The bank can also observe the performance
of interactions in real time, and, based on feedback, self-correct and learn.
In addition to deepening customer
relationships, aggregating behaviour and transaction data in real time and
using machine learning has helped Bank Danamon identify new patterns of fraud and
develop preventive triggers to identify fraud incidents. This enables the bank
to detect potential fraud sooner, send real-time alerts and contact customers
for clarification to reduce losses, thereby improving customer experience and
reducing customer complaints.
Billie Setiawan, head of Decision
Management Data and Analytics, Bank Danamon Indonesia, said, the bank was able
to increase the conversion rate for its marketing campaigns by more than 300
percent, improve customer retention, and reduce the number of fraud incidents
by 30 percent, while significantly lowering costs.
Cloudera and Kogentix, we have the tools to help us test, train, and validate
models, and analyse model performance over time and improve cost efficiency,” he
“A key focus for our digital transformation
at Danamon is to improve customer service while eliminating fraud risks and
compliance cost,” said Mary Bernadette James, chief information officer for
“Big data technology has enabled us to
better manage customer data, while enhancing data protection and managing
compliance. Cloudera’s modern data management platform empowers us to achieve
our digitalisation goals at a lower capital expenditure per terabyte compared
to traditional data management mechanisms, giving us the ability to serve our
customers better and remain competitive in today’s uncertain economic climate.”
Content from Cloudera customer success
story on www.cloudera.com
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