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Generative AI and Quantum Computacional in
Non-Maturity Deposits
Consultancy specializing in Generative AI and quantum computing can enhance forecasting for non-maturity deposits. These technologies can analyze historical data, market trends, and customer behavior to generate more accurate predictions. This information allows financial institutions to effectively manage deposit accounts and optimize interest rates and offerings.
Benefits of Consultancy in Generative AI and QC in Non-Maturity Deposits
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By using Generative AI and quantum computing, consultancy services can assist financial institutions in segmenting their customers for non-maturity deposits. These technologies analyze customer data, financial behavior, and preferences to identify specific customer segments. This, in turn, allows banks to tailor their marketing strategies, promotions, and product offerings to different customer segments. As a result, customer satisfaction and retention rates are likely to increase.
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Financial institutions can optimize their resource allocation for managing non-maturity deposits by consulting in Generative AI and quantum computing. These technologies analyze data on staffing, infrastructure, and operational capacity, which enables banks to allocate their resources efficiently. This, in turn, improves operational efficiency, reduces costs, and ensures smooth deposit management processes.
Compare Consultancy Packages
Package
Description
Scripts (1) in Python or R
Ecometric Models to forecast NMD
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Autoregressive Vector VAR Models
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ARCH Models
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GARCH Models
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Multivariate GARCH Models Copulas
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Vector Error Correction VEC models
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Johansen Method
Deep Learning to forecast NMD
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Long short-term memory LSTM
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Gated Recurrent Units GRU
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CNN + LSTM
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TCN + LSTM/GRU
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Bucketing Assigment
Quantum Machine Learning to forecast NMD without explanatory variables
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Quantum Neural Network
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Quantum Long short term memory LSTM
Forecasting and Scenarios
- Bayesian Long short term memory LSTM
Advanced Forecasting
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DeepAR
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Transformer Model
Generative AI NMD Model
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LLM Model
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Synthetic data
Hours
Lite
We have provided the script to model non maturity deposits using our data with 1 explanatory variables.
Python
Our data: Deposit Volume and one exploratory variable
Our Data with 10 explanatory variables
Our Data with 1 explanatory variables
Our Data with 1 explanatory variables
3
Immediately
3.000 EUR
Standard
We offer a script that can be used to model non-maturity deposits based on your data
Python
Your data comprises deposit volume and three exploratory variables
You can input your data along with up to 10 explanatory variables.
You can input your data along with up to 5 explanatory variables.
You can input your data along with up to 5 explanatory variables.
6
3 Days
5.000 EUR
Pro
We offer a script that can be used to model non-maturity deposits based on your data
Python
Your data comprises deposit volume and three exploratory variables
You can input your data along with up to 25 explanatory variables
You can input your data along with up to 10 explanatory variables.
You can input your data along with up to 5 explanatory variables.
10
5 Days
8.000 EUR
Premium
We offer a script that can be used to model non-maturity deposits based on your data
Python
Your data comprises deposit volume and three exploratory variables
You can input your data along with up to 100 explanatory variables
You can input your data along with up to 10 explanatory variables.
You can input your data along with up to 5 explanatory variables.
15
10 Days
12.000 EUR
1. The scripts are delivered as Jupyter Notebook files
Your data for forecasting and portfolio optimization could be a time series with Date, Volume Deposits, Deposit Rate, Interest Rate y1,.., Interest Rate ym, and other variables too.
Your data for the Churn Model could be ID, Run-Off, Deposit Balance, Explanatory x1,.., Explanatory xm
We accept panel data. The panel data identifier is not considered an explanatory variable.
Please keep in mind the following message regarding the Generative AI Model data we need to discuss with you.
Clients
