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Manceps

MANCEPS
BRINGS COGNITIVE INTELLIGENCE
TO FINANCE & BANKING

WHAT'S DRIVING THE URGENCY TO ADOPT AI?

A need for increased speed and efficiency • The opportunity for deeper data-driven insights • Complex regulations and compliance requirements • Poor customer service and long wait times. • Credit scores not telling the whole story. 

When AI Comes to Finance and Banking —

Novel AI-powered processes and products generate new revenue streams.

Multidimensional and cross-jurisdictional fraud schemes are detected instantly.

Customer acquisition becomes more personalized, streamlined, and data-driven.

Financial reports, compliance assurances, and administrative tasks are completed automatically.

Risk assessment and underwriting goes beyond credit scores.

Claims are validated and losses determined via image processing.

FREE RESOURCE: AI Examples from the World's Biggest Finance and Banking Companies
READ THE GUIDE

APPLICATIONS

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Text Organization & Summarization

Manceps can help financial institutions apply natural language processing to large volumes of text and speech data to extract information, gain insights, and streamline manual tasks. While time and cost savings are obvious benefits, the ability to identify key information (the proverbial needle in the haystack) can make all the difference. Consider bringing automated summarization to legal documents, earnings reports, or job applications.
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Fraud Detection

Fraud detection now involves more than a checklist of risk factors. Using ML techniques, fraud detection systems can now actively learn and calibrate in response to new (or potential) security threats. By analyzing billions of data points, these systems can flag issues that would otherwise go unnoticed by humans, preventing false rejections along the way.
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Personalized Retail Experiences

Customers are becoming increasingly adept at using Chatbots and other conversational interfaces for their banking needs. Such chatbots have to be built using robust natural language processing engines as well as mountains of finance-specific customer interactions. These technologies make it increasingly difficult for bank customers to tell whether they are actually speaking to a human.

 

Chatbots, financial assistants, and related tools are self-learning, which means they increasingly improve with additional customer interactions.

 

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Risk Forecasting

Predictive analytics run on artificial intelligence, and in the financial sector, the introduction of such insights can drive revenue and reduce costs. Financial organizations have used predictive analytics for a variety of purposes. It’s allowed them to identify and target more profitable customers; better manage cashflow; anticipate demand fluctuations, and mitigate risk. As AI becomes increasingly capable, financial organizations are looking to find ever-complex ways to put that data to work.
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Operational Optimization

Like many large organizations, financial institutions are turning to artificial intelligence to automate oft-repeated tasks and to help their business run more smoothly. In one example, reported by McKinsey, JPMorgan began using bots to process internal IT requests, including employees' attempts to reset their work passwords. Up to 1.7 million requests were expected to be handled by the bots in 2017, doing the work of 40 full-time employees.

TAKE THE NEXT STEP.

SPEAK WITH AN EXPERT. 

OUR LATEST RESOURCES
OUR LATEST ARTICLES

DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

Video: How to Build a Reproducible ML Pipeline

Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.

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OUR HEADQUARTERS
Headquartered in the heart of Portland, Oregon, our satellite offices span North America, Europe, the Middle East, and Africa.

(503) 922-1164

Our address is
US Custom House
220 NW 8th Ave
Portland, OR 97209

Copyright © 2019 Manceps